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zichuan-scott-xu/automl-workflow | examples/DeepWisdom/Auto_NLP/deepWisdom/transformers_/__init__.py | d108e55da943775953b9f1801311a86ac07e58a0 | __version__ = "2.1.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
absl.logging.set_verbosity('info')
absl.logging.set_stderrthreshold('info')
absl.logging._warn_preinit_stderr = False
except:
pass
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
# Files and general utilities
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
cached_path, add_start_docstrings, add_end_docstrings,
WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
is_tf_available, is_torch_available)
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
from .tokenization_auto import AutoTokenizer
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_auto import AutoConfig
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
# Modeling
if is_torch_available():
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
AutoModelWithLMHead)
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_ctrl import (CTRLPreTrainedModel, CTRLModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForMultipleChoice,
XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
RobertaForSequenceClassification, RobertaForMultipleChoice,
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_albert import AlbertForSequenceClassification
# Optimization
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
if not is_tf_available() and not is_torch_available():
logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
"Models won't be available and only tokenizers, configuration"
"and file/data utilities can be used.")
| [((17, 9, 17, 36), 'logging.getLogger', 'logging.getLogger', ({(17, 27, 17, 35): '__name__'}, {}), '(__name__)', False, 'import logging\n')] |
TiankunZhou/dials | test/model/data/all_foreground_valid_data.py | bd5c95b73c442cceb1c61b1690fd4562acf4e337 | from __future__ import absolute_import, division, print_function
data = r"""cdials_array_family_flex_ext
shoebox
p1
(tRp2
(cscitbx_array_family_flex_ext
grid
p3
((I0
t(I8
tI01
tRp4
(I8
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tb."""
| [] |
sofieditmer/self-assigned | src/use-model.py | 3033b64d2848fcf73c44dd79ad4e7f07f8387c65 | #!/usr/bin/env python
"""
Info: This script loads the model trained in the cnn-asl.py script and enables the user to use it for classifying unseen ASL letters. It also visualizes the feature map of the last convolutional layer of the network to enable the user to get an insight into exactly which parts of the original image that the model is paying attention to when classifying the image.
Parameters:
(optional) model_name: str <name-of-the-model-to-load>, default = "saved_model.json"
(optional) train_data: str <name-of-training-data>, default = "asl_alphabet_train_subset"
(optional) unseen_image: str <name-of-unseen-image>, default = "unseen_img_test1.png"
Usage:
$ python use-model.py
Output:
- unseen_image_superimposed_heatmap.png: superimposed heatmap on unseen image.
- unseen_image_prediction.txt: model prediction of unseen image.
"""
### DEPENDENCIES ###
# Core libraries
import os
import sys
sys.path.append(os.path.join(".."))
# Matplotlib, numpy, OpenCV
import matplotlib.pyplot as plt
import numpy as np
import cv2
# TensorFlow
import tensorflow as tf
from tensorflow.keras.preprocessing.image import (load_img, img_to_array)
from tensorflow.keras.applications.resnet import preprocess_input
from tensorflow.keras.models import model_from_json
from tensorflow.keras import backend as K
# argparse
import argparse
### MAIN FUNCTION ###
def main():
### ARGPARSE ###
# Initialize ArgumentParser class
ap = argparse.ArgumentParser()
# Argument 1: Model name
ap.add_argument("-m", "--model_name",
type = str,
required = False, # the argument is not required
help = "Name of the model",
default = "saved_model.json") # default name
# Argument 2: Training data
ap.add_argument("-t", "--train_data",
type = str,
required = False, # the argument is not required
help = "Name of training data folder",
default = "asl_alphabet_train_subset") # default is a subset of the training dataset
# Argument 3: Input image
ap.add_argument("-u", "--unseen_image",
type = str,
required = False, # the argument is not required
help = "Name of the image the model should classify",
default = "unseen_img_test1.png") # default unseen image provided in the unseen_images folder
# Parse arguments
args = vars(ap.parse_args())
# Save input parameters
model_name = args["model_name"]
train_data = os.path.join("..", "data", "subset_asl_sign_language", args["train_data"])
unseen_image = args["unseen_image"]
# Create output directory if it does not already exist
if not os.path.exists(os.path.join("..", "output")):
os.mkdir(os.path.join("..", "output"))
# Start message
print("\n[INFO] Initializing...")
# Instantiate the class
classifier = Loaded_model_classifier(train_data, unseen_image)
# Create list of label names from the directory names in the training data folder
labels = classifier.list_labels()
# Load the model
print(f"\n[INFO] Loading the CNN model, {model_name}, from 'output' directory...")
model = classifier.load_model(model_name)
# Classify input image
print(f"\n[INFO] Using the model to predict the class of {unseen_image}...")
label = classifier.classify_unseen_image(labels, model)
# Visualize feature map of network for input image
print(f"\n[INFO] Visualizing the feature map of the last convolutional layer of the network...")
classifier.visualize_feature_map(model)
# User message
print(f"\n[INFO] Done! The {unseen_image} has been classified as {label} and the feature map of the last convolutional layer of the network has been visualized and saved as {unseen_image}_superimposed_heatmap.png in 'output' directory\n")
# Creating classifier class
class Loaded_model_classifier:
def __init__(self, train_data, unseen_image):
# Receive inputs: train data and input image
self.train_data = train_data
self.unseen_image = unseen_image
def list_labels(self):
"""
This method defines the label names by listing the names of the folders within training directory without listing hidden files. It sorts the names alphabetically.
"""
# Create empty list
labels = []
# For every name in training directory
for name in os.listdir(self.train_data):
# If it does not start with . (which hidden files do)
if not name.startswith('.'):
labels.append(name)
# Sort labels alphabetically
labels = sorted(labels)
return labels
def load_model(self, model_name):
"""
This method loads the model and the model weights that are saved in the output directory.
"""
# Load JSON-file and create model
model_path = os.path.join("..", "output", model_name)
json_model = open(model_path, "r")
# Read file
loaded_file = json_model.read()
# Create model
loaded_model = model_from_json(loaded_file)
# Load weights into new model
loaded_model.load_weights(os.path.join("..", "output", "model_weights.h5"))
# Compile model
loaded_model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return loaded_model
def classify_unseen_image(self, labels, model):
"""
This method takes an unseen image, performs some preprocessing to prepare it for the model, and predicts the class of the image using the model.
"""
# Define path
img_path = os.path.join("..", "data", "unseen_images", self.unseen_image)
# Load unseen image
image = load_img(img_path, target_size=(224, 224)) # using the same size as the images the model has been trained on
# Convert the image to a numpy array
image = img_to_array(image)
# Reshape the image, because the model expects a tensor of rank 4. The image goes from being 3-dimensional to 4-dimensional: (1, 224, 224, 3)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
# Prepare the image for the ResNet50 model
image = preprocess_input(image)
# Predict the class of the image
prediction = np.argmax(model.predict(image))
# Convert labels to be a dictionary which is needed to extract the label that corresponds to the prediction
labels = dict(zip(labels, range(len(labels))))
# Define function that finds the key (letter) that corresponds to the predicted value
def find_key(dictionary, value):
return {k for k, v in dictionary.items() if v == value}
# Extract letter that corresponds to the predicted value from the label dictionary
label = find_key(labels, prediction)
# Print the predicted class to the terminal
print(f"\nThe model predicts {self.unseen_image} to be the letter {label}")
# Save prediction as txt-file to output directory
with open(os.path.join("..", "output", f"{self.unseen_image}_prediction.txt"), "w") as f:
f.write(f"The predicted class of the {self.unseen_image} made by the model is {label}")
return label
def visualize_feature_map(self, model):
"""
This method visualizes the feature map of the last convolutional layer of the network.
"""
# Define path
img_path = os.path.join("..", "data", "unseen_images", self.unseen_image)
# Load image with dimensions corresponding to training images
img = load_img(img_path, target_size=(224, 224))
# Convert image to array
x = img_to_array(img)
# Convert to rank 4 tensor
x = np.expand_dims(x, axis=0)
# Preprocess to be in line with ResNet50 data
x = preprocess_input(x)
# Create activation heatmap for final layer. This is done by taking advantage of how the model learns through gradient descent. We use the gradients that have been learned through training, and we go the opposite way (rather than minimizing we are maximizing). Essentially, we make use of the gradients in the final layer to highlight which regions are particularly informative when predicting a given class.
with tf.GradientTape() as tape:
# Take the last convolutional layer in the network
last_conv_layer = model.get_layer('conv5_block3_out')
# Create a model that maps the input image to the activations of the last convolutional layer as well as the output predictions
iterate = tf.keras.models.Model([model.inputs],
[model.output, last_conv_layer.output])
# Compute the gradient of the top predicted class for the input image with respect to the activations of the last conv layer
# Take the gradients from the last layer
model_out, last_conv_layer = iterate(x)
# Find the class that has been predicted by the model
class_out = model_out[:, np.argmax(model_out[0])]
# Extract gradient of the output neuron of the last convolutional layer
grads = tape.gradient(class_out,
last_conv_layer)
# Vector of mean intensity of the gradient over a specific feature map channel
pooled_grads = K.mean(grads, axis=(0, 1, 2))
# Multiply each channel in the feature map array by "how important this channel is" with regard to the top predicted class. Then sum all the channels to obtain the heatmap class activation
heatmap = tf.reduce_mean(tf.multiply(pooled_grads, last_conv_layer), axis=-1)
heatmap = np.maximum(heatmap, 0)
heatmap /= np.max(heatmap)
heatmap = heatmap.reshape((7,7))
plt.matshow(heatmap)
# Load unseen image with OpenCV
img = cv2.imread(img_path)
# Make heatmap semi-transparent
intensity = 0.5
# Resize the heatmap to be the original dimensions of the input
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
# Apply colormap
heatmap = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
# Multiply heatmap by intensity and 'add' this on top of the original image
superimposed = (heatmap * intensity) + img
# Save the superimposed image to output directory
cv2.imwrite(os.path.join("..", "output", f"{self.unseen_image}_superimposed_heatmap.png"), superimposed)
# User message
print(f"\n[INFO] The feature map has now been visualized and superimposed on {self.unseen_image}. Find image as {self.unseen_image}_superimposed_heatmap.png in 'output' directory...")
# Define behaviour when called from command line
if __name__=="__main__":
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(197, 37, 197, 45): '"""output"""', (197, 47, 197, 84): 'f"""{self.unseen_image}_prediction.txt"""'}, {}), "('..', 'output', f'{self.unseen_image}_prediction.txt')", False, 'import os\n'), ((247, 37, 247, 79), 'tensorflow.multiply', 'tf.multiply', ({(247, 49, 247, 61): 'pooled_grads', (247, 63, 247, 78): 'last_conv_layer'}, {}), '(pooled_grads, last_conv_layer)', True, 'import tensorflow as tf\n'), ((263, 40, 263, 61), 'numpy.uint8', 'np.uint8', ({(263, 49, 263, 60): '255 * heatmap'}, {}), '(255 * heatmap)', True, 'import numpy as np\n'), ((269, 24, 269, 101), 'os.path.join', 'os.path.join', ({(269, 37, 269, 41): '""".."""', (269, 43, 269, 51): '"""output"""', (269, 53, 269, 100): 'f"""{self.unseen_image}_superimposed_heatmap.png"""'}, {}), "('..', 'output', f'{self.unseen_image}_superimposed_heatmap.png')", False, 'import os\n'), ((237, 37, 237, 60), 'numpy.argmax', 'np.argmax', ({(237, 47, 237, 59): 'model_out[0]'}, {}), '(model_out[0])', True, 'import numpy as np\n')] |
algorithmiaio/algorithmia-adk-python | examples/hello_world/src/Algorithm.py | 1e5c6b9de08fe34260f3b4c03eb4596cccb4d070 | from Algorithmia import ADK
# API calls will begin at the apply() method, with the request body passed as 'input'
# For more details, see algorithmia.com/developers/algorithm-development/languages
def apply(input):
# If your apply function uses state that's loaded into memory via load, you can pass that loaded state to your apply
# function by defining an additional "globals" parameter in your apply function; but it's optional!
return "hello {}".format(str(input))
# This turns your library code into an algorithm that can run on the platform.
# If you intend to use loading operations, remember to pass a `load` function as a second variable.
algorithm = ADK(apply)
# The 'init()' function actually starts the algorithm, you can follow along in the source code
# to see how everything works.
algorithm.init("Algorithmia")
| [((15, 12, 15, 22), 'Algorithmia.ADK', 'ADK', ({(15, 16, 15, 21): 'apply'}, {}), '(apply)', False, 'from Algorithmia import ADK\n')] |
Xiaoxiong-Liu/gluon-ts | src/gluonts/nursery/autogluon_tabular/estimator.py | 097c492769258dd70b7f223f826b17b0051ceee9 | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import logging
from typing import Callable, Optional, List, Tuple
import pandas as pd
from autogluon.tabular import TabularPredictor as AutogluonTabularPredictor
from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.util import to_pandas
from gluonts.model.estimator import Estimator
from gluonts.time_feature import (
TimeFeature,
get_lags_for_frequency,
time_features_from_frequency_str,
)
from .predictor import (
TabularPredictor,
mean_abs_scaling,
get_features_dataframe,
)
logger = logging.getLogger(__name__)
class TabularEstimator(Estimator):
"""An estimator that trains an Autogluon Tabular model for time series
forecasting.
Additional keyword arguments to the constructor, other than the ones documented
below, will be passed on to Autogluon Tabular's ``fit`` method used for training
the model.
Parameters
----------
freq
Frequency of the data to handle
prediction_length
Prediction length
lag_indices
List of indices of the lagged observations to use as features. If
None, this will be set automatically based on the frequency.
time_features
List of time features to be used. If None, this will be set automatically
based on the frequency.
scaling
Function to be used to scale time series. This should take a pd.Series object
as input, and return a scaled pd.Series and the scale (float). By default,
this divides a series by the mean of its absolute value.
batch_size
Batch size of the resulting predictor; this is just used at prediction
time, and does not affect training in any way.
disable_auto_regression
Whether to forecefully disable auto-regression in the model. If ``True``,
this will remove any lag index which is smaller than ``prediction_length``.
This will make predictions more efficient, but may impact their accuracy.
quantiles_to_predict
Whether to forecast in quantile way. If assigned with quantile values,
this will train model using quantile prediction model. If None, then the model
will be trained in a regular way.
"""
@validated()
def __init__(
self,
freq: str,
prediction_length: int,
lag_indices: Optional[List[int]] = None,
time_features: Optional[List[TimeFeature]] = None,
scaling: Callable[
[pd.Series], Tuple[pd.Series, float]
] = mean_abs_scaling,
batch_size: Optional[int] = 32,
disable_auto_regression: bool = False,
last_k_for_val: Optional[int] = None,
quantiles_to_predict: Optional[List[float]] = None,
eval_metric: str = "mean_absolute_error",
**kwargs,
) -> None:
super().__init__()
self.freq = freq
self.prediction_length = prediction_length
self.lag_indices = (
lag_indices
if lag_indices is not None
else get_lags_for_frequency(self.freq)
)
self.time_features = (
time_features
if time_features is not None
else time_features_from_frequency_str(self.freq)
)
self.batch_size = batch_size
self.disable_auto_regression = disable_auto_regression
self.scaling = scaling
self.last_k_for_val = last_k_for_val
self.eval_metric = eval_metric
self.quantiles_to_predict = quantiles_to_predict
if self.disable_auto_regression:
self.lag_indices = [
lag_idx
for lag_idx in self.lag_indices
if lag_idx >= self.prediction_length
]
default_kwargs = {
"time_limit": 60,
# "excluded_model_types": ["KNN", "XT", "RF"],
"presets": [
"high_quality_fast_inference_only_refit",
"optimize_for_deployment",
],
"auto_stack": True,
}
self.kwargs = {**default_kwargs, **kwargs}
def train(
self,
training_data: Dataset,
validation_data: Optional[Dataset] = None,
) -> TabularPredictor:
kwargs_override = {}
dfs = [
get_features_dataframe(
series=self.scaling(to_pandas(entry))[0],
time_features=self.time_features,
lag_indices=self.lag_indices,
)
for entry in training_data
]
if validation_data is not None or self.last_k_for_val is not None:
kwargs_override["auto_stack"] = False
logger.warning(
"Auto Stacking is turned off "
"as validation dataset is provided before input into Tabular Predictor."
)
if validation_data is not None:
logger.log(20, "Validation dataset is directly provided.")
validation_dfs = [
get_features_dataframe(
series=self.scaling(to_pandas(entry))[0],
time_features=self.time_features,
lag_indices=self.lag_indices,
)
for entry in validation_data
]
train_df = pd.concat(dfs)
val_df = pd.concat(validation_dfs)
elif self.last_k_for_val is not None:
logger.log(
20,
f"last_k_for_val is provided, choosing last {self.last_k_for_val} of each time series as validation set.",
)
train_dfs = [
tmp_df.iloc[: -self.last_k_for_val, :] for tmp_df in dfs
]
validation_dfs = [
tmp_df.iloc[-self.last_k_for_val :, :] for tmp_df in dfs
]
train_df = pd.concat(train_dfs)
val_df = pd.concat(validation_dfs)
else:
logger.log(
20,
"No validation dataset is provided, will let TabularPredictor do the splitting automatically,"
"Note that this might break the time order of time series data.",
)
train_df = pd.concat(dfs)
val_df = None
if self.quantiles_to_predict is not None:
ag_model = AutogluonTabularPredictor(
label="target",
problem_type="quantile",
quantile_levels=self.quantiles_to_predict,
).fit(
train_df,
tuning_data=val_df,
**{**self.kwargs, **kwargs_override},
)
else:
ag_model = AutogluonTabularPredictor(
label="target",
problem_type="regression",
eval_metric=self.eval_metric,
).fit(
train_df,
tuning_data=val_df,
**{**self.kwargs, **kwargs_override},
)
return TabularPredictor(
ag_model=ag_model,
freq=self.freq,
prediction_length=self.prediction_length,
time_features=self.time_features,
lag_indices=self.lag_indices,
scaling=self.scaling,
batch_size=self.batch_size,
quantiles_to_predict=self.quantiles_to_predict,
)
| [((34, 9, 34, 36), 'logging.getLogger', 'logging.getLogger', ({(34, 27, 34, 35): '__name__'}, {}), '(__name__)', False, 'import logging\n'), ((74, 5, 74, 16), 'gluonts.core.component.validated', 'validated', ({}, {}), '()', False, 'from gluonts.core.component import validated\n'), ((98, 17, 98, 50), 'gluonts.time_feature.get_lags_for_frequency', 'get_lags_for_frequency', ({(98, 40, 98, 49): 'self.freq'}, {}), '(self.freq)', False, 'from gluonts.time_feature import TimeFeature, get_lags_for_frequency, time_features_from_frequency_str\n'), ((103, 17, 103, 60), 'gluonts.time_feature.time_features_from_frequency_str', 'time_features_from_frequency_str', ({(103, 50, 103, 59): 'self.freq'}, {}), '(self.freq)', False, 'from gluonts.time_feature import TimeFeature, get_lags_for_frequency, time_features_from_frequency_str\n'), ((163, 23, 163, 37), 'pandas.concat', 'pd.concat', ({(163, 33, 163, 36): 'dfs'}, {}), '(dfs)', True, 'import pandas as pd\n'), ((164, 21, 164, 46), 'pandas.concat', 'pd.concat', ({(164, 31, 164, 45): 'validation_dfs'}, {}), '(validation_dfs)', True, 'import pandas as pd\n'), ((176, 23, 176, 43), 'pandas.concat', 'pd.concat', ({(176, 33, 176, 42): 'train_dfs'}, {}), '(train_dfs)', True, 'import pandas as pd\n'), ((177, 21, 177, 46), 'pandas.concat', 'pd.concat', ({(177, 31, 177, 45): 'validation_dfs'}, {}), '(validation_dfs)', True, 'import pandas as pd\n'), ((184, 23, 184, 37), 'pandas.concat', 'pd.concat', ({(184, 33, 184, 36): 'dfs'}, {}), '(dfs)', True, 'import pandas as pd\n'), ((188, 23, 192, 13), 'autogluon.tabular.TabularPredictor', 'AutogluonTabularPredictor', (), '', True, 'from autogluon.tabular import TabularPredictor as AutogluonTabularPredictor\n'), ((198, 23, 202, 13), 'autogluon.tabular.TabularPredictor', 'AutogluonTabularPredictor', (), '', True, 'from autogluon.tabular import TabularPredictor as AutogluonTabularPredictor\n'), ((140, 36, 140, 52), 'gluonts.dataset.util.to_pandas', 'to_pandas', ({(140, 46, 140, 51): 'entry'}, {}), '(entry)', False, 'from gluonts.dataset.util import to_pandas\n'), ((157, 40, 157, 56), 'gluonts.dataset.util.to_pandas', 'to_pandas', ({(157, 50, 157, 55): 'entry'}, {}), '(entry)', False, 'from gluonts.dataset.util import to_pandas\n')] |
andreas19/dcar | src/dcar/errors.py | 31118ac5924b7cb01f8b7da5a84480824c046df2 | """Errors module."""
__all__ = [
'Error',
'AddressError',
'AuthenticationError',
'TransportError',
'ValidationError',
'RegisterError',
'MessageError',
'DBusError',
'SignatureError',
'TooLongError',
]
class Error(Exception):
"""Base class."""
class AddressError(Error):
"""Raised for errors in server addresses."""
class AuthenticationError(Error):
"""Raised when authentication failed."""
class TransportError(Error):
"""Raised for transport related errors."""
class ValidationError(Error):
"""Raised when validation failed."""
class RegisterError(Error):
"""Raised when a signal or method could not be registered."""
class MessageError(Error):
"""Raised for errors in messages."""
class DBusError(MessageError):
"""Raised for errors from ERROR messages."""
class SignatureError(MessageError):
"""Raised for errors in signatures."""
class TooLongError(MessageError):
"""Raised when a message, an array, a name etc. is too long."""
| [] |
SergeBakharev/content | Packs/CortexXDR/Integrations/XDR_iocs/XDR_iocs_test.py | d66cc274f5bf6f9f0e9ed7e4df1af7b6f305aacf | from XDR_iocs import *
import pytest
from freezegun import freeze_time
Client.severity = 'INFO'
client = Client({'url': 'test'})
def d_sort(in_dict):
return sorted(in_dict.items())
class TestGetHeaders:
@freeze_time('2020-06-01T00:00:00Z')
def test_sanity(self, mocker):
"""
Given:
- API key
- API key ID
Then:
- Verify headers created correct.
"""
params = {
"apikey_id": "7",
"apikey": "t3PkfrEhaRAD9a3r6Lq5cVPyqdMqtLd8cOJlSWUtbslkbERUgb2BTkSNRtDr3C6CWAgYqxvyzwDFJ83BLBgu1V2cxQY7rsoo2ks2u3W2aBL2BlteF8C8u75lCVUrNbv1" # noqa: E501
}
headers = {
'Authorization': 'da94963b561e3c95899d843b1284cecf410606e9e809be528ec1cf03880c6e9e',
'x-iocs-source': 'xsoar',
'x-xdr-auth-id': '7',
'x-xdr-nonce': '1111111111111111111111111111111111111111111111111111111111111111',
'x-xdr-timestamp': '1590969600000'
}
mocker.patch('secrets.choice', return_value='1')
output = get_headers(params)
assert output == headers, f'get_headers({params})\n\treturns: {d_sort(output)}\n\tinstead: {d_sort(headers)}'
def test_empty_case(self):
"""
Given:
Empty params
Then:
get_headers will not raise error
"""
get_headers({})
class TestHttpRequest:
class Res:
content = 'error'.encode()
def __init__(self, code):
self.status_code = code
@staticmethod
def json():
return {}
XDR_SERVER_ERROR = 500
INVALID_CREDS = 401
LICENSE_ERROR = 402
PERMISSION_ERROR = 403
OK = 200
data_test_http_request_error_codes = [
(OK, {}),
(XDR_SERVER_ERROR, 'XDR internal server error.\t(error)'),
(INVALID_CREDS, 'Unauthorized access. An issue occurred during authentication. This can indicate an incorrect key, id, or other invalid authentication parameters.\t(error)'), # noqa: E501
(LICENSE_ERROR, 'Unauthorized access. User does not have the required license type to run this API.\t(error)'),
(PERMISSION_ERROR, 'Unauthorized access. The provided API key does not have the required RBAC permissions to run this API.\t(error)') # noqa: E501
]
@pytest.mark.parametrize('res, expected_output', data_test_http_request_error_codes)
def test_http_request_error_codes(self, res, expected_output, mocker):
"""
Given:
- Status code
When:
- http_request returns this status code.
Then:
- Verify error/success format.
"""
mocker.patch('requests.post', return_value=self.Res(res))
try:
output = client.http_request('', {})
except DemistoException as error:
output = str(error)
assert output == expected_output, f'status code {res}\n\treturns: {output}\n\tinstead: {expected_output}'
class TestGetRequestsKwargs:
def test_with_file(self, mocker):
"""
Given:
- file to upload
Then:
- Verify output format.
"""
def override_open(open_path, *_other):
return open_path
mocker.patch('builtins.open', side_effect=override_open)
path = '/Users/some_user/some_dir/some_file.file'
output = get_requests_kwargs(file_path=path)
expected_output = {'files': [('file', ('iocs.json', path, 'application/json'))]}
assert output == expected_output, f'get_requests_kwargs(file_path={path})\n\treturns: {output}\n\t instead: {expected_output}' # noqa: E501
def test_with_json(self):
"""
Given:
- simple json
Then:
- the json ready to send
"""
_json = {'test': 'test'}
output = get_requests_kwargs(_json=_json)
expected_output = {'data': '{"request_data": {"test": "test"}}'}
assert output == expected_output, f'get_requests_kwargs(_json={_json})\n\treturns: {output}\n\t instead: {expected_output}' # noqa: E501
class TestPrepareCommands:
def test_prepare_get_changes(self):
"""
Given:
- get changes command
Then:
- Verify url and json format.
"""
ts = int(datetime.now(timezone.utc).timestamp() * 1000)
url_suffix, _json = prepare_get_changes(ts)
assert url_suffix == 'get_changes', f'prepare_get_changes\n\treturns url_suffix: {url_suffix}\n\tinstead url_suffix: get_changes' # noqa: E501
assert _json == {'last_update_ts': ts}
def test_prepare_enable_iocs(self):
"""
Given:
- enable iocs command
Then:
- Verify url and json format.
"""
url_suffix, iocs = prepare_enable_iocs('8.8.8.8,domain.com')
assert url_suffix == 'enable_iocs', f'prepare_enable_iocs\n\treturns url_suffix: {url_suffix}\n\tinstead url_suffix: enable_iocs' # noqa: E501
assert iocs == ['8.8.8.8', 'domain.com']
def test_prepare_disable_iocs(self):
"""
Given:
- disable iocs command
Then:
- Verify url and json format.
"""
url_suffix, iocs = prepare_disable_iocs('8.8.8.8,domain.com')
assert url_suffix == 'disable_iocs', f'prepare_disable_iocs\n\treturns url_suffix: {url_suffix}\n\tinstead url_suffix: disable_iocs' # noqa: E501
assert iocs == ['8.8.8.8', 'domain.com']
class TestCreateFile:
path = 'test_data/sync_file_test.json'
data_test_create_file_sync = [
('Domain_iocs', 'Domain_sync_file'),
('IP_iocs', 'IP_sync_file'),
('File_iocs', 'File_sync_file')
]
data_test_create_file_iocs_to_keep = [
('Domain_iocs', 'Domain_iocs_to_keep_file'),
('IP_iocs', 'IP_iocs_to_keep_file'),
('File_iocs', 'File_iocs_to_keep_file')
]
def setup(self):
# creates the file
with open(TestCreateFile.path, 'w') as _file:
_file.write('')
def teardown(self):
# removes the file when done
os.remove(TestCreateFile.path)
@staticmethod
def get_file(path):
with open(path, 'r') as _file:
return _file.read()
@staticmethod
def get_all_iocs(go_over, extension):
iocs = []
total = 0
data = []
for in_iocs, out_iocs in go_over:
ioc = json.loads(TestCreateFile.get_file(f'test_data/{in_iocs}.json'))
iocs.extend(ioc['iocs'])
total += ioc['total']
data.append(TestCreateFile.get_file(f'test_data/{out_iocs}.{extension}'))
all_iocs = {'iocs': iocs, 'total': total}
all_data = ''.join(data)
return all_iocs, all_data
def test_create_file_sync_without_iocs(self, mocker):
"""
Given:
- Sync command
When:
- there is no iocs
Then:
- Verify sync file data.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value={})
create_file_sync(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
expected_data = ''
assert data == expected_data, f'create_file_sync with no iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
@pytest.mark.parametrize('in_iocs, out_iocs', data_test_create_file_sync)
def test_create_file_sync(self, in_iocs, out_iocs, mocker):
"""
Given:
- Sync command
When:
- iocs type is a specific type.
Then:
- Verify sync file data.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value=json.loads(self.get_file(f'test_data/{in_iocs}.json'))) # noqa: E501
create_file_sync(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
expected_data = self.get_file(f'test_data/{out_iocs}.txt')
assert data == expected_data, f'create_file_sync with {in_iocs} iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
def test_create_file_sync_all_types(self, mocker):
"""
Given:
- Sync command
When:
- iocs as all types
Then:
- Verify sync file data.
"""
all_iocs, expected_data = self.get_all_iocs(self.data_test_create_file_sync, 'txt')
mocker.patch.object(demisto, 'searchIndicators', return_value=all_iocs)
create_file_sync(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
assert data == expected_data, f'create_file_sync with all iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
data_test_create_file_with_empty_indicators = [
{},
{'value': '11.11.11.11'},
{'indicator_type': 'IP'}
]
@pytest.mark.parametrize('defective_indicator', data_test_create_file_with_empty_indicators)
def test_create_file_sync_with_empty_indicators(self, defective_indicator, mocker):
"""
Given:
- Sync command
When:
- a part iocs dont have all required data
Then:
- Verify sync file data.
"""
all_iocs, expected_data = self.get_all_iocs(self.data_test_create_file_sync, 'txt')
all_iocs['iocs'].append(defective_indicator)
all_iocs['total'] += 1
mocker.patch.object(demisto, 'searchIndicators', return_value=all_iocs)
warnings = mocker.patch.object(demisto, 'debug')
create_file_sync(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
assert data == expected_data, f'create_file_sync with all iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
error_msg = warnings.call_args.args[0]
assert error_msg.startswith("unexpected IOC format in key: '"), f"create_file_sync empty message\n\tstarts: {error_msg}\n\tinstead: unexpected IOC format in key: '" # noqa: E501
assert error_msg.endswith(f"', {str(defective_indicator)}"), f"create_file_sync empty message\n\tends: {error_msg}\n\tinstead: ', {str(defective_indicator)}" # noqa: E501
def test_create_file_iocs_to_keep_without_iocs(self, mocker):
"""
Given:
- iocs to keep command
When:
- there is no iocs
Then:
- Verify iocs to keep file data.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value={})
create_file_iocs_to_keep(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
expected_data = ''
assert data == expected_data, f'create_file_iocs_to_keep with no iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
@pytest.mark.parametrize('in_iocs, out_iocs', data_test_create_file_iocs_to_keep)
def test_create_file_iocs_to_keep(self, in_iocs, out_iocs, mocker):
"""
Given:
- iocs to keep command
When:
- iocs type is a specific type.
Then:
- Verify iocs to keep file data.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value=json.loads(
self.get_file(f'test_data/{in_iocs}.json')))
create_file_iocs_to_keep(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
expected_data = self.get_file(f'test_data/{out_iocs}.txt')
assert data == expected_data, f'create_file_iocs_to_keep with {in_iocs} iocs\n\tcreates: {data}\n\tinstead: {expected_data}' # noqa: E501
def test_create_file_iocs_to_keep_all_types(self, mocker):
"""
Given:
- iocs to keep command
When:
- iocs as all types
Then:
- Verify iocs to keep file data.
"""
all_iocs, expected_data = self.get_all_iocs(self.data_test_create_file_iocs_to_keep, 'txt')
mocker.patch.object(demisto, 'searchIndicators', return_value=all_iocs)
create_file_iocs_to_keep(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
assert data == expected_data, f'create_file_iocs_to_keep with all iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
class TestDemistoIOCToXDR:
data_test_demisto_expiration_to_xdr = [
(None, -1),
('', -1),
('0001-01-01T00:00:00Z', -1),
('2020-06-03T00:00:00Z', 1591142400000)
]
@pytest.mark.parametrize('demisto_expiration, xdr_expiration', data_test_demisto_expiration_to_xdr)
def test_demisto_expiration_to_xdr(self, demisto_expiration, xdr_expiration):
"""
Given:
- demisto indicator expiration
Then:
- Verify XDR expiration.
"""
output = demisto_expiration_to_xdr(demisto_expiration)
assert xdr_expiration == output, f'demisto_expiration_to_xdr({demisto_expiration})\n\treturns: {output}\n\tinstead: {xdr_expiration}' # noqa: E501
data_test_demisto_reliability_to_xdr = [
(None, 'F'),
('A - Completely reliable', 'A'),
('B - Usually reliable', 'B'),
('C - Fairly reliable', 'C'),
('D - Not usually reliable', 'D'),
('E - Unreliable', 'E'),
('F - Reliability cannot be judged', 'F')
]
@pytest.mark.parametrize('demisto_reliability, xdr_reliability', data_test_demisto_reliability_to_xdr)
def test_demisto_reliability_to_xdr(self, demisto_reliability, xdr_reliability):
"""
Given:
- demisto indicator reliability
Then:
- Verify XDR reliability.
"""
output = demisto_reliability_to_xdr(demisto_reliability)
assert output == xdr_reliability, f'demisto_reliability_to_xdr({demisto_reliability})\n\treturns: {output}\n\tinstead: {xdr_reliability}' # noqa: E501
data_test_demisto_types_to_xdr = [
('File', 'HASH'),
('IP', 'IP'),
('Domain', 'DOMAIN_NAME')
]
@pytest.mark.parametrize('demisto_type, xdr_type', data_test_demisto_types_to_xdr)
def test_demisto_types_to_xdr(self, demisto_type, xdr_type):
"""
Given:
- demisto indicator type
Then:
- Verify XDR type.
"""
output = demisto_types_to_xdr(demisto_type)
assert output == xdr_type, f'demisto_reliability_to_xdr({demisto_type})\n\treturns: {output}\n\tinstead: {xdr_type}'
data_test_demisto_vendors_to_xdr = [
(
{'moduleID': {'sourceBrand': 'test', 'reliability': 'A - Completely reliable', 'score': 2}},
{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}
),
(
{'moduleID': {'reliability': 'A - Completely reliable', 'score': 2}},
{'vendor_name': 'moduleID', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}
),
(
{'moduleID': {'sourceBrand': 'test', 'score': 2}},
{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'F'}
),
(
{'moduleID': {'reliability': 'A - Completely reliable', 'score': 0}},
{'vendor_name': 'moduleID', 'reputation': 'UNKNOWN', 'reliability': 'A'}
)
]
@pytest.mark.parametrize('demisto_vendor, xdr_vendor', data_test_demisto_vendors_to_xdr)
def test_demisto_vendors_to_xdr(self, demisto_vendor, xdr_vendor):
"""
Given:
- demisto indicator vendors reports.
Then:
- Verify XDR vendors format.
"""
output = demisto_vendors_to_xdr(demisto_vendor)[0]
assert output == xdr_vendor, f'demisto_vendors_to_xdr({demisto_vendor})\n\treturns: {d_sort(output)}\n\tinstead: {d_sort(xdr_vendor)}' # noqa: E501
data_test_demisto_ioc_to_xdr = [
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO',
'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 100, 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO', 'type': '100'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP'},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'expiration': '2020-06-03T00:00:00Z'},
{'expiration_date': 1591142400000, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentTimeLine', 'content': 'test'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentRegular', 'content': 'test'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'comment': 'test'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentRegular', 'content': 'test'}, {'type': 'IndicatorCommentRegular', 'content': 'this is the comment'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'comment': 'this is the comment'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'aggregatedReliability': 'A - Completely reliable'},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'reliability': 'A'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'CustomFields': {'threattypes': {'threatcategory': 'Malware'}}}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'class': 'Malware'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'moduleToFeedMap': {'module': {'sourceBrand': 'test', 'score': 2}}}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'vendors': [{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'F'}]} # noqa: E501
)
]
@pytest.mark.parametrize('demisto_ioc, xdr_ioc', data_test_demisto_ioc_to_xdr)
def test_demisto_ioc_to_xdr(self, demisto_ioc, xdr_ioc):
"""
Given:
- demisto indicator.
Then:
- Verify XDR indicator format.
"""
output = demisto_ioc_to_xdr(demisto_ioc)
assert output == xdr_ioc, f'demisto_ioc_to_xdr({demisto_ioc})\n\treturns: {d_sort(output)}\n\tinstead: {d_sort(xdr_ioc)}' # noqa: E501
def test_empty_demisto_ioc_to_xdr(self, mocker):
warnings = mocker.patch.object(demisto, 'debug')
output = demisto_ioc_to_xdr({})
assert output == {}, 'demisto_ioc_to_xdr({})\n\treturns: ' + str(d_sort(output)) + '\n\tinstead: {}'
assert warnings.call_args.args[0] == "unexpected IOC format in key: 'value', {}"
class TestXDRIOCToDemisto:
data_test_xdr_expiration_to_demisto = [
(-1, 'Never'),
(1591142400000, '2020-06-03T00:00:00Z'),
(1592142400000, '2020-06-14T13:46:40Z')
]
@pytest.mark.parametrize('xdr_expiration, demisto_expiration', data_test_xdr_expiration_to_demisto)
def test_xdr_expiration_to_demisto(self, xdr_expiration, demisto_expiration):
"""
Given:
- expiration in XDR format.
Then:
- expiration in demisto format.
"""
output = xdr_expiration_to_demisto(xdr_expiration)
assert output == demisto_expiration, f'xdr_expiration_to_demisto({xdr_expiration})\n\treturns: {output}\n\tinstead: {demisto_expiration}' # noqa: E501
data_test_xdr_ioc_to_demisto = [
(
{
'RULE_ID': 863, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'DISABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801230, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'HASH',
'RULE_INDICATOR': 'fa66f1e0e318b6d7b595b6cee580dc0d8e4ac38fbc8dbfcac6ad66dbe282832e', 'REPUTATION': 'GOOD', # noqa: E501
'RELIABILITY': None, 'VENDORS': None, 'KLASS': None, 'IS_DEFAULT_TTL': False, 'RULE_TTL': -1,
'MARKED_DELETED': 0
},
{
'value': 'fa66f1e0e318b6d7b595b6cee580dc0d8e4ac38fbc8dbfcac6ad66dbe282832e',
'type': 'File',
'score': 1,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'disabled'
}
}
),
(
{
'RULE_ID': 861, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'DISABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801784, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'DOMAIN_NAME', 'RULE_INDICATOR': 'test.com', 'REPUTATION': 'GOOD', # noqa: E501
'RELIABILITY': None, 'VENDORS': None, 'KLASS': None, 'IS_DEFAULT_TTL': False, 'RULE_TTL': -1,
'MARKED_DELETED': 0
},
{
'value': 'test.com',
'type': 'Domain',
'score': 1,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'disabled'
}
}
),
(
{
'RULE_ID': 862, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'ENABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801784, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'DOMAIN_NAME', 'RULE_INDICATOR': 'test.co.il',
'REPUTATION': 'SUSPICIOUS', 'RELIABILITY': 'A',
'VENDORS': [{'vendor_name': 'Cortex XDR - IOC', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}],
'KLASS': None,
'IS_DEFAULT_TTL': False, 'RULE_TTL': -1, 'MARKED_DELETED': 0
},
{
'value': 'test.co.il',
'type': 'Domain',
'score': 2,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'enabled'
}
}
)
]
@pytest.mark.parametrize('xdr_ioc, demisto_ioc', data_test_xdr_ioc_to_demisto)
def test_xdr_ioc_to_demisto(self, xdr_ioc, demisto_ioc, mocker):
"""
Given:
- IOC in XDR format.
Then:
- IOC in demisto format.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value={})
output = xdr_ioc_to_demisto(xdr_ioc)
del output['rawJSON']
assert output == demisto_ioc, f'xdr_ioc_to_demisto({xdr_ioc})\n\treturns: {d_sort(output)}\n\tinstead: {d_sort(demisto_ioc)}' # noqa: E501
class TestCommands:
# test commands full flow
class TestIOCSCommand:
def test_iocs_command_with_enable(self, mocker):
"""
Given:
- enable command
Then:
- Verify enable command is called.
"""
mocker.patch.object(demisto, 'command', return_value='xdr-iocs-enable')
mocker.patch.object(demisto, 'args', return_value={'indicator': '11.11.11.11'})
mocker.patch('XDR_iocs.Client.http_request', return_value={})
outputs = mocker.patch('XDR_iocs.return_outputs')
enable_ioc = mocker.patch('XDR_iocs.prepare_enable_iocs', side_effect=prepare_enable_iocs)
iocs_command(client)
output = outputs.call_args.args[0]
assert output == 'indicators 11.11.11.11 enabled.', f'enable command\n\tprints: {output}\n\tinstead: indicators 11.11.11.11 enabled.' # noqa: E501
assert enable_ioc.call_count == 1, 'enable command not called'
def test_iocs_command_with_disable(self, mocker):
"""
Given:
- disable command
Then:
- Verify disable command is called.
"""
mocker.patch.object(demisto, 'command', return_value='xdr-iocs-disable')
mocker.patch.object(demisto, 'args', return_value={'indicator': '11.11.11.11'})
mocker.patch('XDR_iocs.Client.http_request', return_value={})
outputs = mocker.patch('XDR_iocs.return_outputs')
disable_ioc = mocker.patch('XDR_iocs.prepare_disable_iocs', side_effect=prepare_disable_iocs)
iocs_command(client)
output = outputs.call_args.args[0]
assert output == 'indicators 11.11.11.11 disabled.', f'disable command\n\tprints: {output}\n\tinstead: indicators 11.11.11.11 disabled.' # noqa: E501
assert disable_ioc.call_count == 1, 'disable command not called'
def test_sync(self, mocker):
http_request = mocker.patch.object(Client, 'http_request')
iocs, data = TestCreateFile.get_all_iocs(TestCreateFile.data_test_create_file_sync, 'txt')
mocker.patch.object(demisto, 'searchIndicators', returnvalue=iocs)
mocker.patch('XDR_iocs.return_outputs')
sync(client)
assert http_request.call_args.args[0] == 'sync_tim_iocs', 'sync command url changed'
@freeze_time('2020-06-03T02:00:00Z')
def test_iocs_to_keep(self, mocker):
http_request = mocker.patch.object(Client, 'http_request')
iocs, data = TestCreateFile.get_all_iocs(TestCreateFile.data_test_create_file_iocs_to_keep, 'txt')
mocker.patch.object(demisto, 'searchIndicators', returnvalue=iocs)
mocker.patch('XDR_iocs.return_outputs')
iocs_to_keep(client)
assert http_request.call_args.args[0] == 'iocs_to_keep', 'iocs_to_keep command url changed'
def test_tim_insert_jsons(self, mocker):
http_request = mocker.patch.object(Client, 'http_request')
mocker.patch.object(demisto, 'getIntegrationContext', return_value={'time': '2020-06-03T00:00:00Z'})
iocs, _ = TestCreateFile.get_all_iocs(TestCreateFile.data_test_create_file_sync, 'txt')
mocker.patch.object(demisto, 'searchIndicators', return_value=iocs)
mocker.patch('XDR_iocs.return_outputs')
tim_insert_jsons(client)
assert http_request.call_args.kwargs['url_suffix'] == 'tim_insert_jsons/', 'tim_insert_jsons command url changed'
def test_get_changes(self, mocker):
mocker.patch.object(demisto, 'getIntegrationContext', return_value={'ts': 1591142400000})
mocker.patch.object(demisto, 'createIndicators')
mocker.patch.object(demisto, 'searchIndicators', return_value={})
xdr_res = {'reply': list(map(lambda xdr_ioc: xdr_ioc[0], TestXDRIOCToDemisto.data_test_xdr_ioc_to_demisto))}
mocker.patch.object(Client, 'http_request', return_value=xdr_res)
get_changes(client)
xdr_ioc_to_timeline(list(map(lambda x: str(x[0].get('RULE_INDICATOR')), TestXDRIOCToDemisto.data_test_xdr_ioc_to_demisto))) # noqa: E501
class TestParams:
tags_test = [
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO',
'type': 'IP'},
{'tlp_color': ''},
'Cortex XDR',
None
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO',
'type': 'IP'},
{'tag': 'tag1'},
'tag1',
None
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO',
'type': 'IP'},
{'feedTags': 'tag2', 'tlp_color': 'AMBER'},
'tag2',
'AMBER'
)
]
@pytest.mark.parametrize('demisto_ioc, xdr_ioc, param_value, expected_tags, expected_tlp_color', tags_test)
def test_feed_tags_and_tlp_color(self, demisto_ioc, xdr_ioc, param_value, expected_tags, expected_tlp_color, mocker):
"""
Given:
- IOC in XDR format.
Then:
- IOC in demisto format.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value={})
mocker.patch.object(demisto, 'params', return_value=param_value)
mocker.patch.object(demisto, 'getIntegrationContext', return_value={'ts': 1591142400000})
mocker.patch.object(demisto, 'searchIndicators', return_value={})
outputs = mocker.patch.object(demisto, 'createIndicators')
Client.tag = demisto.params().get('feedTags', demisto.params().get('tag', Client.tag))
Client.tlp_color = demisto.params().get('tlp_color')
client = Client({'url': 'yana'})
xdr_res = {'reply': list(map(lambda xdr_ioc: xdr_ioc[0], TestXDRIOCToDemisto.data_test_xdr_ioc_to_demisto))}
mocker.patch.object(Client, 'http_request', return_value=xdr_res)
get_changes(client)
output = outputs.call_args.args[0]
assert output[0]['fields']['tags'] == expected_tags
assert output[0]['fields'].get('trafficlightprotocol') == expected_tlp_color
| [((15, 5, 15, 40), 'freezegun.freeze_time', 'freeze_time', ({(15, 17, 15, 39): '"""2020-06-01T00:00:00Z"""'}, {}), "('2020-06-01T00:00:00Z')", False, 'from freezegun import freeze_time\n'), ((73, 5, 73, 88), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(73, 29, 73, 51): '"""res, expected_output"""', (73, 53, 73, 87): 'data_test_http_request_error_codes'}, {}), "('res, expected_output',\n data_test_http_request_error_codes)", False, 'import pytest\n'), ((217, 5, 217, 77), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(217, 29, 217, 48): '"""in_iocs, out_iocs"""', (217, 50, 217, 76): 'data_test_create_file_sync'}, {}), "('in_iocs, out_iocs', data_test_create_file_sync)", False, 'import pytest\n'), ((254, 5, 254, 96), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(254, 29, 254, 50): '"""defective_indicator"""', (254, 52, 254, 95): 'data_test_create_file_with_empty_indicators'}, {}), "('defective_indicator',\n data_test_create_file_with_empty_indicators)", False, 'import pytest\n'), ((292, 5, 292, 85), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(292, 29, 292, 48): '"""in_iocs, out_iocs"""', (292, 50, 292, 84): 'data_test_create_file_iocs_to_keep'}, {}), "('in_iocs, out_iocs', data_test_create_file_iocs_to_keep\n )", False, 'import pytest\n'), ((334, 5, 334, 103), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(334, 29, 334, 65): '"""demisto_expiration, xdr_expiration"""', (334, 67, 334, 102): 'data_test_demisto_expiration_to_xdr'}, {}), "('demisto_expiration, xdr_expiration',\n data_test_demisto_expiration_to_xdr)", False, 'import pytest\n'), ((356, 5, 356, 106), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(356, 29, 356, 67): '"""demisto_reliability, xdr_reliability"""', (356, 69, 356, 105): 'data_test_demisto_reliability_to_xdr'}, {}), "('demisto_reliability, xdr_reliability',\n data_test_demisto_reliability_to_xdr)", False, 'import pytest\n'), ((374, 5, 374, 86), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(374, 29, 374, 53): '"""demisto_type, xdr_type"""', (374, 55, 374, 85): 'data_test_demisto_types_to_xdr'}, {}), "('demisto_type, xdr_type',\n data_test_demisto_types_to_xdr)", False, 'import pytest\n'), ((405, 5, 405, 92), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(405, 29, 405, 57): '"""demisto_vendor, xdr_vendor"""', (405, 59, 405, 91): 'data_test_demisto_vendors_to_xdr'}, {}), "('demisto_vendor, xdr_vendor',\n data_test_demisto_vendors_to_xdr)", False, 'import pytest\n'), ((461, 5, 461, 82), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(461, 29, 461, 51): '"""demisto_ioc, xdr_ioc"""', (461, 53, 461, 81): 'data_test_demisto_ioc_to_xdr'}, {}), "('demisto_ioc, xdr_ioc', data_test_demisto_ioc_to_xdr)", False, 'import pytest\n'), ((488, 5, 488, 103), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(488, 29, 488, 65): '"""xdr_expiration, demisto_expiration"""', (488, 67, 488, 102): 'data_test_xdr_expiration_to_demisto'}, {}), "('xdr_expiration, demisto_expiration',\n data_test_xdr_expiration_to_demisto)", False, 'import pytest\n'), ((565, 5, 565, 82), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(565, 29, 565, 51): '"""xdr_ioc, demisto_ioc"""', (565, 53, 565, 81): 'data_test_xdr_ioc_to_demisto'}, {}), "('xdr_ioc, demisto_ioc', data_test_xdr_ioc_to_demisto)", False, 'import pytest\n'), ((625, 5, 625, 40), 'freezegun.freeze_time', 'freeze_time', ({(625, 17, 625, 39): '"""2020-06-03T02:00:00Z"""'}, {}), "('2020-06-03T02:00:00Z')", False, 'from freezegun import freeze_time\n'), ((681, 5, 681, 111), 'pytest.mark.parametrize', 'pytest.mark.parametrize', ({(681, 29, 681, 99): '"""demisto_ioc, xdr_ioc, param_value, expected_tags, expected_tlp_color"""', (681, 101, 681, 110): 'tags_test'}, {}), "(\n 'demisto_ioc, xdr_ioc, param_value, expected_tags, expected_tlp_color',\n tags_test)", False, 'import pytest\n')] |
rchdlps/django-docker | project/users/models.py | 2c12732264c1f17cd62e20927b5956db498c30b7 | from django.contrib.auth.models import AbstractUser
from django.db.models import CharField
from django.urls import reverse
from django.utils.translation import ugettext_lazy as _
from django.db import models
from PIL import Image
class User(AbstractUser):
# First Name and Last Name do not cover name patterns
# around the globe.
name = CharField(_('Nome de usuário:'), blank=True, max_length=255)
# Profile Models
image = models.ImageField(verbose_name='Foto de Perfil:',
default='default.jpg', upload_to='profile_pics')
birth_date = models.DateField(_('Data de Nascimento:'), null=True, blank=True)
cpf = models.CharField(_('CPF:'), max_length=50, blank=True)
cnpj = models.CharField(_('CNPJ:'), max_length=50, blank=True)
bio = models.TextField(_('Descrição:'), blank=True, default='')
cep = models.CharField(_('CEP:'), max_length=50, blank=True)
street = models.CharField(_('Rua:'), max_length=100, blank=True)
number_home = models.CharField(_('Número:'), max_length=10, blank=True)
neighborhood = models.CharField(_('Bairro:'), max_length=100, blank=True)
city = models.CharField(_('Cidade:'), max_length=50, blank=True)
state = models.CharField(_('Estado:'), max_length=50, blank=True)
phone = models.CharField(_('Telefone:'), max_length=50, blank=True)
cel_phone = models.CharField(_('Celular:'), max_length=50, blank=True)
def get_absolute_url(self):
return reverse("users:detail", kwargs={"username": self.username})
"""def save(self):
super().save()
img = Image.open(self.image.path)
if img.height > 300 or img.width > 300:
output_size = (300, 300)
img.thumbnail(output_size)
img.save(self.image.path)"""
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cloudify-incubator/cloudify-plugins-sdk | cloudify_terminal_sdk/netconf_connection.py | 9805008e739d31e5f9fe3184411648f9be5e6214 | # Copyright (c) 2015-2020 Cloudify Platform Ltd. All rights reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from cloudify_common_sdk import exceptions
from cloudify_terminal_sdk import base_connection
# final of any package
NETCONF_1_0_END = "]]>]]>"
# base level of communication
NETCONF_1_0_CAPABILITY = 'urn:ietf:params:netconf:base:1.0'
# package based communication
NETCONF_1_1_CAPABILITY = 'urn:ietf:params:netconf:base:1.1'
class NetConfConnection(base_connection.SSHConnection):
# ssh connection
ssh = None
chan = None
# buffer for same packages, will save partial packages between calls
buff = ""
current_level = NETCONF_1_0_CAPABILITY
def connect(
self, ip, user, hello_string, password=None, key_content=None,
port=830
):
"""open connection and send xml string by link"""
self._ssh_connect(ip, user, password, key_content, port)
self.conn = self.ssh.get_transport().open_session()
self.conn.invoke_subsystem('netconf')
self.buff = ""
capabilities = self.send(hello_string)
return capabilities
def send(self, xml):
"""send xml string by connection"""
if self.current_level == NETCONF_1_1_CAPABILITY:
self._send_1_1(xml)
return self._recv_1_1()
else:
self._send_1_0(xml)
return self._recv_1_0()
def _send_1_0(self, xml):
"""send xml string with NETCONF_1_0_END by connection"""
if xml:
message = xml + NETCONF_1_0_END
self._conn_send(message)
def _recv_1_0(self):
"""recv xml string with NETCONF_1_0_END by connection"""
while self.buff.find(NETCONF_1_0_END) == -1:
self.buff += self._conn_recv(8192)
if self.conn.closed:
break
package_end = self.buff.find(NETCONF_1_0_END)
# we have already closed connection
if package_end == -1:
package_end = len(self.buff)
response = self.buff[:package_end]
self.buff = self.buff[package_end + len(NETCONF_1_0_END):]
return response
def _send_1_1(self, xml):
"""send xml string as package by connection"""
if xml:
message = "\n#{0}\n".format(len(xml))
message += xml
message += "\n##\n"
self._conn_send(message)
def _recv_1_1(self):
"""send xml string as package by connection"""
get_everything = False
response = ""
while not get_everything:
if len(self.buff) < 2:
self.buff += self._conn_recv(2)
# skip new line
if self.buff[:2] != "\n#":
# We have already closed connection
# caller shoud stop to ask new messages
if not self.buff and self.conn.closed:
return ""
raise exceptions.NonRecoverableError("no start")
self.buff = self.buff[2:]
# get package length
while self.buff.find("\n") == -1:
self.buff += self._conn_recv(20)
if self.buff[:2] == "#\n":
get_everything = True
self.buff = self.buff[2:]
break
length = int(self.buff[:self.buff.find("\n")])
self.buff = self.buff[self.buff.find("\n") + 1:]
# load current package
while length > len(self.buff):
self.buff += self._conn_recv(length - len(self.buff))
response += self.buff[:length]
self.buff = self.buff[length:]
return response
def close(self, goodbye_string=None):
"""send xml string by link and close connection"""
response = None
if goodbye_string:
# we have something to say
response = self.send(goodbye_string)
self._ssh_close()
return response
| [((99, 22, 99, 64), 'cloudify_common_sdk.exceptions.NonRecoverableError', 'exceptions.NonRecoverableError', ({(99, 53, 99, 63): '"""no start"""'}, {}), "('no start')", False, 'from cloudify_common_sdk import exceptions\n')] |
dyt1990/Seis_DCEC | Seismic_Conv1D_dec.py | 6cc56a7db10dd87b0ef39ece73578fca8b23c55f | # -*- coding: utf-8 -*-
"""
Created on Sun Aug 19 17:48:13 2018
@author: Sediment
"""
# -*- coding: utf-8 -*-
'''
Keras implementation of deep embedder to improve clustering, inspired by:
"Unsupervised Deep Embedding for Clustering Analysis" (Xie et al, ICML 2016)
Definition can accept somewhat custom neural networks. Defaults are from paper.
'''
import sys
import numpy as np
import pandas as pd
import keras.backend as K
from keras.initializers import RandomNormal
from keras.engine.topology import Layer, InputSpec
from keras.models import Model, Sequential
from keras.layers import Dense, Dropout, Input, Conv1D, MaxPooling1D, BatchNormalization, Activation, Flatten, UpSampling1D, Reshape
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Nadam
from keras.regularizers import l2
from sklearn.preprocessing import normalize
from keras.callbacks import LearningRateScheduler
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
if (sys.version[0] == 2):
import cPickle as pickle
else:
import pickle
class ClusteringLayer(Layer):
'''
Clustering layer which converts latent space Z of input layer
into a probability vector for each cluster defined by its centre in
Z-space. Use Kullback-Leibler divergence as loss, with a probability
target distribution.
# Arguments
output_dim: int > 0. Should be same as number of clusters.
input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument `input_shape`)
is required when using this layer as the first layer in a model.
weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape `(input_dim, output_dim)`
and (output_dim,) for weights and biases respectively.
alpha: parameter in Student's t-distribution. Default is 1.0.
# Input shape
2D tensor with shape: `(nb_samples, input_dim)`.
# Output shape
2D tensor with shape: `(nb_samples, output_dim)`.
'''
def __init__(self, output_dim, input_dim=None, weights=None, alpha=1.0, **kwargs):
self.output_dim = output_dim
self.input_dim = input_dim
self.alpha = alpha
# kmeans cluster centre locations
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(ClusteringLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[1]
self.input_spec = [InputSpec(dtype=K.floatx(),
shape=(None, input_dim))]
self.W = K.variable(self.initial_weights)
self.trainable_weights = [self.W]
def call(self, x, mask=None):
q = 1.0/(1.0 + K.sqrt(K.sum(K.square(K.expand_dims(x, 1) - self.W), axis=2))**2 /self.alpha)
q = q**((self.alpha+1.0)/2.0)
q = K.transpose(K.transpose(q)/K.sum(q, axis=1))
return q
def get_output_shape_for(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], self.output_dim)
def get_config(self):
config = {'output_dim': self.output_dim,
'input_dim': self.input_dim}
base_config = super(ClusteringLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class DeepEmbeddingClustering(object):
def __init__(self,
n_clusters,
input_dim,
learning_rate=0.1,
encoded=None,
decoded=None,
alpha=1.0,
pretrained_weights=None,
cluster_centres=None,
batch_size=256,
conv_filters=[8, 16, 32],
kernel_size=12,
Maxpooling_size=2,
LatentSpace_Z=25,
finetune_epochs=5,
**kwargs):
super(DeepEmbeddingClustering, self).__init__()
self.n_clusters = n_clusters
self.input_dim = input_dim
self.encoded = encoded
self.decoded = decoded
self.alpha = alpha
self.pretrained_weights = pretrained_weights
self.cluster_centres = cluster_centres
self.batch_size = batch_size
self.learning_rate = learning_rate
self.iters_lr_update = 6000
self.lr_change_rate = 0.1
self.finetune_epochs = finetune_epochs
self.conv_filters = conv_filters
self.kernel_size = kernel_size
self.Maxpooling_size = Maxpooling_size
self.LatentSpace_Z = LatentSpace_Z
self.encoders = []
self.decoders = []
input_data = Input(shape=(self.input_dim, 1))
x = Conv1D(self.conv_filters[0], (self.kernel_size), activation='relu', padding='same')(input_data)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = MaxPooling1D((self.Maxpooling_size), padding='same')(x)
x = Conv1D(self.conv_filters[1], (self.kernel_size), activation='relu', padding='same')(x)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = MaxPooling1D((self.Maxpooling_size), padding='same')(x)
x = Conv1D(self.conv_filters[2], (self.kernel_size), activation='relu', padding='same')(x)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = MaxPooling1D((self.Maxpooling_size), padding='same')(x)
# at this point the representation is (16 x conv_filters) i.e. 128-dimensional
x = Flatten()(x)
# at this point the representation is (6) i.e. 128-dimensional
encoded = Dense(LatentSpace_Z, activation='relu')(x)
# 256 = input_data / ((2^maxpool_num) * conv_fileters * 4)
x = Dense(self.input_dim // (2**3) * self.conv_filters[2], kernel_initializer=RandomNormal(mean=0.0, stddev=0.01, seed=None),
bias_initializer='zeros', activation='relu')(encoded)
x = Reshape((self.input_dim // (2**3), self.conv_filters[2]))(x) # 16 * 2 * 2 * 2 = 128, 多少个maxpool就与多少个2相乘
x = Conv1D(self.conv_filters[2], (self.kernel_size), activation='relu', padding='same')(x)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = UpSampling1D((self.Maxpooling_size))(x)
x = Conv1D(self.conv_filters[1], (self.kernel_size), activation='relu', padding='same')(x)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = UpSampling1D((self.Maxpooling_size))(x)
x = Conv1D(self.conv_filters[0], (1), activation='relu')(x)
# x = BatchNormalization()(x)
# x = Activation('relu')(x)
x = UpSampling1D((self.Maxpooling_size))(x)
decoded = Conv1D(1, (self.kernel_size), activation='relu', padding='same')(x)
self.autoencoder = Model(input_data, decoded)
self.autoencoder.summary()
self.encoder = Model(input_data, encoded)
# build the end-to-end autoencoder for finetuning
# Note that at this point dropout is discarded
self.encoder.compile(loss='mse', optimizer=SGD(lr=self.learning_rate, decay=0, momentum=0.9))
self.autoencoder.compile(loss='mse', optimizer=SGD(lr=self.learning_rate, decay=0, momentum=0.9))
if cluster_centres is not None:
assert cluster_centres.shape[0] == self.n_clusters
assert cluster_centres.shape[1] == self.encoder.layers[-1].output_dim
if self.pretrained_weights is not None:
self.autoencoder.load_weights(self.pretrained_weights)
def p_mat(self, q):
weight = q**2 / q.sum(0)
return (weight.T / weight.sum(1)).T
def initialize(self, X, save_autoencoder=False, finetune_iters=5000):
if self.pretrained_weights is None:
iters_per_epoch = int(len(X) / self.batch_size)
print('layerwise pretrain')
lr_epoch_update = max(1, self.iters_lr_update / float(iters_per_epoch))
def step_decay(epoch):
initial_rate = self.learning_rate
factor = int(epoch / lr_epoch_update)
lr = initial_rate / (10 ** factor)
return lr
lr_schedule = LearningRateScheduler(step_decay)
#update encoder and decoder weights:
self.autoencoder.fit(X, X, batch_size=self.batch_size, epochs=self.finetune_epochs, callbacks=[lr_schedule])
if save_autoencoder:
self.autoencoder.save_weights('autoencoder.h5')
else:
print('Loading pretrained weights for autoencoder.')
self.autoencoder.load_weights(self.pretrained_weights)
# update encoder, decoder
# TODO: is this needed? Might be redundant...
for i in range(len(self.encoder.layers)):
self.encoder.layers[i].set_weights(self.autoencoder.layers[i].get_weights())
# initialize cluster centres using k-means
print('Initializing cluster centres with k-means.')
if self.cluster_centres is None:
np.random.seed(42) #随机种子，用于初始化聚类中心
kmeans = KMeans(n_clusters=self.n_clusters, max_iter=100, n_init=6, precompute_distances='auto', random_state=None, tol=1e-4)
self.y_pred = kmeans.fit_predict(self.encoder.predict(X))
self.cluster_centres = kmeans.cluster_centers_
print ('cluster_centres:\n ', self.cluster_centres)
# prepare DCEC model
self.DCEC = Sequential([self.encoder,
ClusteringLayer(self.n_clusters,
weights=self.cluster_centres,
name='clustering')])
self.DCEC.compile(loss='kullback_leibler_divergence', optimizer=SGD(lr=self.learning_rate, decay=0, momentum=0.9))
# loss: 'mean_squared_error', 'categorical_crossentropy', 'hinge', 'squared_hinge'
return
def visualizeData(self, Z, labels, num_clusters, csv_filename, title):
'''
TSNE visualization of the points in latent space Z
:param Z: Numpy array containing points in latent space in which clustering was performed
:param labels: True labels - used for coloring points
:param num_clusters: Total number of clusters
:param title: filename where the plot should be saved
:return: None - (side effect) saves clustering visualization plot in specified location
'''
print ('Start visualizing Data')
labels = labels.astype(int)
tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
Z_tsne = tsne.fit_transform(Z)
fig = plt.figure()
plt.scatter(Z_tsne[:, 0], Z_tsne[:, 1], s=2, c=labels, cmap=plt.cm.get_cmap("jet", num_clusters))
plt.colorbar(ticks=range(num_clusters))
# fig.savefig(title, dpi=fig.dpi)
fig.savefig(title, dpi=600)
# save t_sne results
print('Save t_sne results')
dataframe = pd.DataFrame({'Z_tsne_x':Z_tsne[:, 0], 'Z_tsne_y':Z_tsne[:, 1], 'labels':labels})
dataframe.to_csv(csv_filename, index=False, sep=',')
def cluster(self, X, y=None,
tol=0.001, update_interval=None,
iter_max=799,
save_interval=None,
**kwargs):
if update_interval is None:
# 1 epochs
update_interval = X.shape[0]/self.batch_size
print('Update interval', update_interval)
if save_interval is None:
# 50 epochs
save_interval = X.shape[0]/self.batch_size*50
print('Save interval', save_interval)
assert save_interval >= update_interval
train = True
iteration, index = 0, 0
self.accuracy = []
while train:
sys.stdout.write('\r')
# cutoff iteration
if iter_max < iteration:
print('Reached maximum iteration limit. Stopping training.')
return self.y_pred
# update (or initialize) probability distributions and propagate weight changes
# from DCEC model to encoder.
if iteration % update_interval == 0:
self.q = self.DCEC.predict(X, verbose=0)
self.p = self.p_mat(self.q)
y_pred = self.q.argmax(1)
delta_label = ((y_pred == self.y_pred).sum().astype(np.float32) / y_pred.shape[0])
if y is None:
print(str(np.round(delta_label*100, 5))+'% change in label assignment')
if iteration > 0 and delta_label < tol:
print('delta_label ', delta_label, '< tol ', tol)
print('Reached tolerance threshold. Stopping training.')
train = False
continue
else:
self.y_pred = y_pred
for i in range(len(self.encoder.layers)):
self.encoder.layers[i].set_weights(self.DCEC.layers[0].layers[i].get_weights())
self.cluster_centres = self.DCEC.layers[-1].get_weights()[0]
# train on batch
sys.stdout.write('Iteration %d, ' % iteration)
if (index+1)*self.batch_size >= X.shape[0]:
loss = self.DCEC.train_on_batch(X[index*self.batch_size::], self.p[index*self.batch_size::])
index = 0
sys.stdout.write('Loss %f\n' % loss)
else:
loss = self.DCEC.train_on_batch(X[index*self.batch_size:(index+1) * self.batch_size],
self.p[index*self.batch_size:(index+1) * self.batch_size])
sys.stdout.write('Loss %f\n' % loss)
index += 1
# save intermediate
if iteration % save_interval == 0:
z = self.encoder.predict(X)
pca = PCA(n_components=2).fit(z)
z_2d = pca.transform(z)
clust_2d = pca.transform(self.cluster_centres)
# save states for visualization
pickle.dump({'z_2d': z_2d, 'clust_2d': clust_2d, 'q': self.q, 'p': self.p},
open('c'+str(iteration)+'.pkl', 'wb'))
# save DCEC model checkpoints
self.DCEC.save('DCEC_model_'+str(iteration)+'.h5')
iteration += 1
sys.stdout.flush()
return y_pred
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xiguadong/ppq | ppq/utils/round.py | 6c71adb3c2a8ca95967f101724b5e4b3e6f761ff | from decimal import ROUND_HALF_DOWN, ROUND_HALF_EVEN, ROUND_HALF_UP, Decimal
from math import ceil, floor, log2
from typing import Union
import torch
from ppq.core import RoundingPolicy
def ppq_numerical_round(value: float,
policy: RoundingPolicy=RoundingPolicy.ROUND_HALF_EVEN) -> int:
"""
reference: https://en.wikipedia.org/wiki/Rounding
decimal defination:
- decimal.ROUND_CEILING (towards Infinity)
- decimal.ROUND_DOWN (towards zero)
- decimal.ROUND_FLOOR (towards -Infinity)
- decimal.ROUND_HALF_DOWN (to nearest with ties going towards zero)
- decimal.ROUND_HALF_EVEN (to nearest with ties going to nearest even integer)
- decimal.ROUND_HALF_UP (to nearest with ties going away from zero)
- decimal.ROUND_UP (away from zero)
- decimal.ROUND_05UP (away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise towards zero)
Args:
value (float): [description]
policy (RoundingPolicy, optional): [description]. Defaults to RoundingPolicy.ROUND_HALF_EVEN.
Raises:
ValueError: [description]
Returns:
int: [description]
"""
assert isinstance(value, float), 'numerical round only takes effect on float number.'
if policy == RoundingPolicy.ROUND_HALF_EVEN:
return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_EVEN))
elif policy == RoundingPolicy.ROUND_HALF_UP:
if value > 0: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_UP))
else: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_DOWN))
elif policy == RoundingPolicy.ROUND_HALF_DOWN:
if value > 0: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_DOWN))
else: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_UP))
elif policy == RoundingPolicy.ROUND_HALF_TOWARDS_ZERO:
return ppq_numerical_round(value, RoundingPolicy.ROUND_HALF_DOWN)
elif policy == RoundingPolicy.ROUND_HALF_FAR_FORM_ZERO:
return ppq_numerical_round(value, RoundingPolicy.ROUND_HALF_UP)
elif policy == RoundingPolicy.ROUND_TO_NEAR_INT:
if value > 0: return floor(value + 0.5)
else: return ceil(value - 0.5)
elif policy == RoundingPolicy.ROUND_UP:
return ceil(value)
else:
raise ValueError('Unexpected rounding policy found.')
def ppq_tensor_round(value: torch.Tensor,
policy:RoundingPolicy=RoundingPolicy.ROUND_HALF_EVEN) -> torch.Tensor:
"""
reference: https://en.wikipedia.org/wiki/Rounding
Args:
value (torch.Tensor): [description]
policy (RoundingPolicy, optional): [description]. Defaults to RoundingPolicy.ROUND_HALF_EVEN.
Raises:
ValueError: [description]
Returns:
torch.Tensor: [description]
"""
assert isinstance(value, torch.Tensor), 'tensor round only takes effect on torch tensor.'
if policy == RoundingPolicy.ROUND_HALF_EVEN:
# default rounding policy of torch is ROUND_TO_NEAR_EVEN
# try this: print(torch.Tensor([1.5, 2.5, 3.5, 4.5]).round())
# However it may generate unexpected results due to version difference.
return value.round()
elif policy == RoundingPolicy.ROUND_UP:
return value.ceil()
elif policy == RoundingPolicy.ROUND_HALF_TOWARDS_ZERO:
return torch.sign(value) * torch.ceil(value.abs() - 0.5)
elif policy == RoundingPolicy.ROUND_HALF_FAR_FORM_ZERO:
return torch.sign(value) * torch.floor(value.abs() + 0.5)
elif policy == RoundingPolicy.ROUND_HALF_DOWN:
return torch.ceil(value - 0.5)
elif policy == RoundingPolicy.ROUND_HALF_UP:
return torch.floor(value + 0.5)
elif policy == RoundingPolicy.ROUND_TO_NEAR_INT:
raise NotImplementedError(f'Torch Tensor can not use this rounding policy({policy}) try ROUND_HALF_EVEN instead.')
else:
raise ValueError('Unexpected rounding policy found.')
def ppq_round_to_power_of_2(value: Union[float, int],
policy: RoundingPolicy=RoundingPolicy.ROUND_UP) -> float:
if value == 0: return 0
sign = 1 if value >= 0 else -1
assert isinstance(value, float) or isinstance(value, int), \
'power-of-2 round only takes effect on float or int.'
return sign * float(pow(2, ppq_numerical_round(log2(sign * value), policy=policy)))
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maropu/scavenger | python/repair/train.py | 03a935968f4aa507d4d98c8ca528195b770757d9 | #!/usr/bin/env python3
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import copy
import time
import numpy as np # type: ignore[import]
import pandas as pd # type: ignore[import]
from collections import namedtuple
from typing import Any, Dict, List, Optional, Tuple
from repair.utils import elapsed_time, get_option_value, setup_logger
_logger = setup_logger()
# List of internal configurations
_option = namedtuple('_option', 'key default_value type_class validator err_msg')
_opt_boosting_type = \
_option('model.lgb.boosting_type', 'gbdt', str,
lambda v: v in ['gbdt', 'dart', 'goss', 'rf'], "`{}` should be in ['gbdt', 'dart', 'goss', 'rf']")
_opt_class_weight = \
_option('model.lgb.class_weight', 'balanced', str, None, None)
_opt_learning_rate = \
_option('model.lgb.learning_rate', 0.01, float,
lambda v: v > 0.0, '`{}` should be positive')
_opt_max_depth = \
_option('model.lgb.max_depth', 7, int, None, None)
_opt_max_bin = \
_option('model.lgb.max_bin', 255, int, None, None)
_opt_reg_alpha = \
_option('model.lgb.reg_alpha', 0.0, float,
lambda v: v >= 0.0, '`{}` should be greater than or equal to 0.0')
_opt_min_split_gain = \
_option('model.lgb.min_split_gain', 0.0, float,
lambda v: v >= 0.0, '`{}` should be greater than or equal to 0.0')
_opt_n_estimators = \
_option('model.lgb.n_estimators', 300, int,
lambda v: v > 0, '`{}` should be positive')
_opt_importance_type = \
_option('model.lgb.importance_type', 'gain', str,
lambda v: v in ['split', 'gain'], "`{}` should be in ['split', 'gain']")
_opt_n_splits = \
_option('model.cv.n_splits', 3, int,
lambda v: v >= 3, '`{}` should be greater than 2')
_opt_timeout = \
_option('model.hp.timeout', 0, int, None, None)
_opt_max_evals = \
_option('model.hp.max_evals', 100000000, int,
lambda v: v > 0, '`{}` should be positive')
_opt_no_progress_loss = \
_option('model.hp.no_progress_loss', 50, int,
lambda v: v > 0, '`{}` should be positive')
train_option_keys = [
_opt_boosting_type.key,
_opt_class_weight.key,
_opt_learning_rate.key,
_opt_max_depth.key,
_opt_max_bin.key,
_opt_reg_alpha.key,
_opt_min_split_gain.key,
_opt_n_estimators.key,
_opt_importance_type.key,
_opt_n_splits.key,
_opt_timeout.key,
_opt_max_evals.key,
_opt_no_progress_loss.key
]
@elapsed_time # type: ignore
def _build_lgb_model(X: pd.DataFrame, y: pd.Series, is_discrete: bool, num_class: int, n_jobs: int,
opts: Dict[str, str]) -> Tuple[Any, float]:
import lightgbm as lgb # type: ignore[import]
def _get_option_value(*args) -> Any: # type: ignore
return get_option_value(opts, *args)
if is_discrete:
objective = "binary" if num_class <= 2 else "multiclass"
else:
objective = "regression"
fixed_params = {
"boosting_type": _get_option_value(*_opt_boosting_type),
"objective": objective,
"class_weight": _get_option_value(*_opt_class_weight),
"learning_rate": _get_option_value(*_opt_learning_rate),
"max_depth": _get_option_value(*_opt_max_depth),
"max_bin": _get_option_value(*_opt_max_bin),
"reg_alpha": _get_option_value(*_opt_reg_alpha),
"min_split_gain": _get_option_value(*_opt_min_split_gain),
"n_estimators": _get_option_value(*_opt_n_estimators),
"importance_type": _get_option_value(*_opt_importance_type),
"random_state": 42,
"n_jobs": n_jobs
}
# Set `num_class` only in the `multiclass` mode
if objective == "multiclass":
fixed_params["num_class"] = num_class
model_class = lgb.LGBMClassifier if is_discrete \
else lgb.LGBMRegressor
def _create_model(params: Dict[str, Any]) -> Any:
# Some params must be int
for k in ["num_leaves", "subsample_freq", "min_child_samples"]:
if k in params:
params[k] = int(params[k])
p = copy.deepcopy(fixed_params)
p.update(params)
return model_class(**p)
from hyperopt import hp, tpe, Trials # type: ignore[import]
from hyperopt.early_stop import no_progress_loss # type: ignore[import]
from hyperopt.fmin import fmin # type: ignore[import]
from sklearn.model_selection import ( # type: ignore[import]
cross_val_score, KFold, StratifiedKFold
)
# TODO: Temporality supress `sklearn.model_selection` user's warning
import warnings
warnings.simplefilter("ignore", UserWarning)
# Forcibly disable INFO-level logging in the `hyperopt` module
from logging import getLogger, WARN
getLogger("hyperopt").setLevel(WARN)
param_space = {
"num_leaves": hp.quniform("num_leaves", 2, 100, 1),
"subsample": hp.uniform("subsample", 0.5, 1.0),
"subsample_freq": hp.quniform("subsample_freq", 1, 20, 1),
"colsample_bytree": hp.uniform("colsample_bytree", 0.01, 1.0),
"min_child_samples": hp.quniform("min_child_samples", 1, 50, 1),
"min_child_weight": hp.loguniform("min_child_weight", -3, 1),
"reg_lambda": hp.loguniform("reg_lambda", -2, 3)
}
scorer = "f1_macro" if is_discrete else "neg_mean_squared_error"
n_splits = int(_get_option_value(*_opt_n_splits))
cv = StratifiedKFold(n_splits=n_splits, shuffle=True) if is_discrete \
else KFold(n_splits=n_splits, shuffle=True)
def _objective(params: Dict[str, Any]) -> float:
model = _create_model(params)
fit_params: Dict[str, str] = {
# TODO: Raises an error if a single regressor is used
# "categorical_feature": "auto",
}
try:
# TODO: Replace with `lgb.cv` to remove the `sklearn` dependency
scores = cross_val_score(
model, X, y, scoring=scorer, cv=cv, fit_params=fit_params, n_jobs=n_jobs)
return -scores.mean()
# it might throw an exception because `y` contains
# previously unseen labels.
except Exception as e:
_logger.warning(f"{e.__class__}: {e}")
return 0.0
def _early_stop_fn() -> Any:
no_progress_loss_fn = no_progress_loss(int(_get_option_value(*_opt_no_progress_loss)))
timeout = int(_get_option_value(*_opt_timeout))
if timeout <= 0:
return no_progress_loss_fn
# Set base time for budget mechanism
start_time = time.time()
def timeout_fn(trials, best_loss=None, iteration_no_progress=0): # type: ignore
no_progress_loss, meta = no_progress_loss_fn(trials, best_loss, iteration_no_progress)
to = time.time() - start_time > timeout
return no_progress_loss or to, meta
return timeout_fn
try:
trials = Trials()
max_evals = int(_get_option_value(*_opt_max_evals))
best_params = fmin(
fn=_objective,
space=param_space,
algo=tpe.suggest,
trials=trials,
max_evals=max_evals,
early_stop_fn=_early_stop_fn(),
rstate=np.random.RandomState(42),
show_progressbar=False,
verbose=False)
_logger.info("hyperopt: #eval={}/{}".format(len(trials.trials), max_evals))
# Builds a model with `best_params`
# TODO: Could we extract constraint rules (e.g., FD and CFD) from built statistical models?
model = _create_model(best_params)
model.fit(X, y)
def _feature_importances() -> List[Any]:
f = filter(lambda x: x[1] > 0.0, zip(model.feature_name_, model.feature_importances_))
return list(sorted(f, key=lambda x: x[1], reverse=True))
_logger.debug(f"lightgbm: feature_importances={_feature_importances()}")
sorted_lst = sorted(trials.trials, key=lambda x: x['result']['loss'])
min_loss = sorted_lst[0]['result']['loss']
return model, -min_loss
except Exception as e:
_logger.warning(f"Failed to build a stat model because: {e}")
return None, 0.0
def build_model(X: pd.DataFrame, y: pd.Series, is_discrete: bool, num_class: int, n_jobs: int,
opts: Dict[str, str]) -> Tuple[Any, float]:
return _build_lgb_model(X, y, is_discrete, num_class, n_jobs, opts)
def compute_class_nrow_stdv(y: pd.Series, is_discrete: bool) -> Optional[float]:
from collections import Counter
return float(np.std(list(map(lambda x: x[1], Counter(y).items())))) if is_discrete else None
def rebalance_training_data(X: pd.DataFrame, y: pd.Series, target: str) -> Tuple[pd.DataFrame, pd.Series]:
# Uses median as the number of training rows for each class
from collections import Counter
prev_nrows = len(X)
prev_stdv = compute_class_nrow_stdv(y, is_discrete=True)
hist = dict(Counter(y).items()) # type: ignore
median = int(np.median([count for key, count in hist.items()]))
def _split_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series]:
X = df[df.columns[df.columns != target]] # type: ignore
y = df[target]
return X, y
# Filters out rows having NaN values for over-sampling
X[target] = y
X_notna, y_notna = _split_data(X.dropna())
X_na, y_na = _split_data(X[X.isnull().any(axis=1)])
# Over-sampling for training data whose row number is smaller than the median value
hist_na = dict(Counter(y_na).items()) # type: ignore
smote_targets = []
kn = 5 # `k_neighbors` default value in `SMOTEN`
for key, count in hist.items():
if count < median:
nna = hist_na[key] if key in hist_na else 0
if count - nna > kn:
smote_targets.append((key, median - nna))
else:
_logger.warning(f"Over-sampling of '{key}' in y='{target}' failed because the number of the clean rows "
f"is too small: {count - nna}")
if len(smote_targets) > 0:
from imblearn.over_sampling import SMOTEN
sampler = SMOTEN(random_state=42, sampling_strategy=dict(smote_targets), k_neighbors=kn)
X_notna, y_notna = sampler.fit_resample(X_notna, y_notna)
X = pd.concat([X_notna, X_na])
y = pd.concat([y_notna, y_na])
# Under-sampling for training data whose row number is greater than the median value
rus_targets = list(map(lambda x: (x[0], median), filter(lambda x: x[1] > median, hist.items())))
if len(rus_targets) > 0:
# NOTE: The other smarter implementations can skew samples if there are many rows having NaN values,
# so we just use `RandomUnderSampler` here.
from imblearn.under_sampling import RandomUnderSampler
sampler = RandomUnderSampler(random_state=42, sampling_strategy=dict(rus_targets))
X, y = sampler.fit_resample(X, y)
_logger.info("Rebalanced training data (y={}, median={}): #rows={}(stdv={}) -> #rows={}(stdv={})".format(
target, median, prev_nrows, prev_stdv, len(X), compute_class_nrow_stdv(y, is_discrete=True)))
_logger.debug("class hist: {} => {}".format(hist.items(), Counter(y).items()))
return X, y
| [((30, 10, 30, 24), 'repair.utils.setup_logger', 'setup_logger', ({}, {}), '()', False, 'from repair.utils import elapsed_time, get_option_value, setup_logger\n'), ((34, 10, 34, 81), 'collections.namedtuple', 'namedtuple', ({(34, 21, 34, 30): '"""_option"""', (34, 32, 34, 80): '"""key default_value type_class validator err_msg"""'}, {}), "('_option', 'key default_value type_class validator err_msg')", False, 'from collections import namedtuple\n'), ((142, 4, 142, 48), 'warnings.simplefilter', 'warnings.simplefilter', ({(142, 26, 142, 34): '"""ignore"""', (142, 36, 142, 47): 'UserWarning'}, {}), "('ignore', UserWarning)", False, 'import warnings\n'), ((278, 8, 278, 34), 'pandas.concat', 'pd.concat', ({(278, 18, 278, 33): '[X_notna, X_na]'}, {}), '([X_notna, X_na])', True, 'import pandas as pd\n'), ((279, 8, 279, 34), 'pandas.concat', 'pd.concat', ({(279, 18, 279, 33): '[y_notna, y_na]'}, {}), '([y_notna, y_na])', True, 'import pandas as pd\n'), ((95, 15, 95, 44), 'repair.utils.get_option_value', 'get_option_value', ({(95, 32, 95, 36): 'opts', (95, 38, 95, 43): '*args'}, {}), '(opts, *args)', False, 'from repair.utils import elapsed_time, get_option_value, setup_logger\n'), ((129, 12, 129, 39), 'copy.deepcopy', 'copy.deepcopy', ({(129, 26, 129, 38): 'fixed_params'}, {}), '(fixed_params)', False, 'import copy\n'), ((149, 22, 149, 58), 'hyperopt.hp.quniform', 'hp.quniform', ({(149, 34, 149, 46): '"""num_leaves"""', (149, 48, 149, 49): '(2)', (149, 51, 149, 54): '(100)', (149, 56, 149, 57): '(1)'}, {}), "('num_leaves', 2, 100, 1)", False, 'from hyperopt import hp, tpe, Trials\n'), ((150, 21, 150, 54), 'hyperopt.hp.uniform', 'hp.uniform', ({(150, 32, 150, 43): '"""subsample"""', (150, 45, 150, 48): '(0.5)', (150, 50, 150, 53): '(1.0)'}, {}), "('subsample', 0.5, 1.0)", False, 'from hyperopt import hp, tpe, Trials\n'), ((151, 26, 151, 65), 'hyperopt.hp.quniform', 'hp.quniform', ({(151, 38, 151, 54): '"""subsample_freq"""', (151, 56, 151, 57): '(1)', (151, 59, 151, 61): '(20)', (151, 63, 151, 64): '(1)'}, {}), "('subsample_freq', 1, 20, 1)", False, 'from hyperopt import hp, tpe, Trials\n'), ((152, 28, 152, 69), 'hyperopt.hp.uniform', 'hp.uniform', ({(152, 39, 152, 57): '"""colsample_bytree"""', (152, 59, 152, 63): '(0.01)', (152, 65, 152, 68): '(1.0)'}, {}), "('colsample_bytree', 0.01, 1.0)", False, 'from hyperopt import hp, tpe, Trials\n'), ((153, 29, 153, 71), 'hyperopt.hp.quniform', 'hp.quniform', ({(153, 41, 153, 60): '"""min_child_samples"""', (153, 62, 153, 63): '(1)', (153, 65, 153, 67): '(50)', (153, 69, 153, 70): '(1)'}, {}), "('min_child_samples', 1, 50, 1)", False, 'from hyperopt import hp, tpe, Trials\n'), ((154, 28, 154, 68), 'hyperopt.hp.loguniform', 'hp.loguniform', ({(154, 42, 154, 60): '"""min_child_weight"""', (154, 62, 154, 64): '(-3)', (154, 66, 154, 67): '(1)'}, {}), "('min_child_weight', -3, 1)", False, 'from hyperopt import hp, tpe, Trials\n'), ((155, 22, 155, 56), 'hyperopt.hp.loguniform', 'hp.loguniform', ({(155, 36, 155, 48): '"""reg_lambda"""', (155, 50, 155, 52): '(-2)', (155, 54, 155, 55): '(3)'}, {}), "('reg_lambda', -2, 3)", False, 'from hyperopt import hp, tpe, Trials\n'), ((160, 9, 160, 57), 'sklearn.model_selection.StratifiedKFold', 'StratifiedKFold', (), '', False, 'from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n'), ((161, 13, 161, 51), 'sklearn.model_selection.KFold', 'KFold', (), '', False, 'from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n'), ((188, 21, 188, 32), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((198, 17, 198, 25), 'hyperopt.Trials', 'Trials', ({}, {}), '()', False, 'from hyperopt import hp, tpe, Trials\n'), ((146, 4, 146, 25), 'logging.getLogger', 'getLogger', ({(146, 14, 146, 24): '"""hyperopt"""'}, {}), "('hyperopt')", False, 'from logging import getLogger, WARN\n'), ((171, 21, 172, 89), 'sklearn.model_selection.cross_val_score', 'cross_val_score', (), '', False, 'from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold\n'), ((207, 19, 207, 44), 'numpy.random.RandomState', 'np.random.RandomState', ({(207, 41, 207, 43): '42'}, {}), '(42)', True, 'import numpy as np\n'), ((247, 16, 247, 26), 'collections.Counter', 'Counter', ({(247, 24, 247, 25): 'y'}, {}), '(y)', False, 'from collections import Counter\n'), ((261, 19, 261, 32), 'collections.Counter', 'Counter', ({(261, 27, 261, 31): 'y_na'}, {}), '(y_na)', False, 'from collections import Counter\n'), ((192, 17, 192, 28), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((292, 62, 292, 72), 'collections.Counter', 'Counter', ({(292, 70, 292, 71): 'y'}, {}), '(y)', False, 'from collections import Counter\n'), ((239, 49, 239, 59), 'collections.Counter', 'Counter', ({(239, 57, 239, 58): 'y'}, {}), '(y)', False, 'from collections import Counter\n')] |
volzotan/django-howl | howl/roomsensor/urls.py | 3b11c530da95d152844934da09592619b3d4497f | from django.conf.urls import patterns, url
from roomsensor import views
urlpatterns = patterns('',
url(r'^$', views.index, name='roomsensor'),
# ex: /roomsensor/name/
url(r'^(?P<roomsensor_name>\w+)/$', views.display, name='roomsensor_display'),
url(r'^(?P<roomsensor_name>\w+)/read/$', views.read, name='roomsensor_read'),
# JSON data for graph creation
url(r'^(?P<roomsensor_name>\w+)/rawdata/(?P<datapoints>\d+)/(?P<compression_factor>\d+)/$', views.rawdata, name='roomsensor_rawdata'),
) | [((6, 4, 6, 46), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import patterns, url\n'), ((9, 4, 9, 81), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import patterns, url\n'), ((10, 4, 10, 80), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import patterns, url\n'), ((13, 4, 13, 137), 'django.conf.urls.url', 'url', (), '', False, 'from django.conf.urls import patterns, url\n')] |
vu-telab/DAKOTA-moga-post-processing-tool | main.py | 2f41561bd8ca44c693e5994f7f68a1edc1a82361 | # main.py
#
# currently just an example script I use to test my optimization_results module
#
# WARNING: design point numbers 0-indexed in pandas database, but
# eval_id column is the original 1-indexed value given by DAKOTA
import optimization_results as optr
def main():
a4 = optr.MogaOptimizationResults()
print a4.gen_size_list
print a4.pareto_front
assert a4.gen_size_list == [100, 94, 48, 45, 45, 46, 62, 85, 102, 108, 131, 130, 134, 119,
127, 128, 155, 124, 124, 130, 128, 123, 137, 135, 149, 165, 154,
164, 169, 177, 205, 196, 215, 185, 205, 190, 162, 158, 154, 159,
163, 183, 175, 183, 186, 188, 188, 186, 201, 213, 222]
### OLD MATLAB CODE I NEED TO REWORK ###
# # read force and atan accuracy objectives from
# # all_accuracy_objectives.dat
# A3 = load('all_accuracy_objectives.dat');
# completed_points = A3(:,1);
# force_objs = A3(:,2);
# atan_objs = A3(:,3);
# n3 = length(A3(:,1));
if __name__=='__main__':
main()
| [] |
Rhodolite/Gem.py.UnitTest | Topaz/Core.py | eaa8b6855bcfbb12f67e7eb146928814543ef9d4 | #
# Copyright (c) 2017 Joy Diamond. All rights reserved.
#
@gem('Topaz.Core')
def gem():
require_gem('Gem.Global')
from Gem import gem_global
gem_global.testing = true
require_gem('Gem.Cache2')
require_gem('Gem.DumpCache')
require_gem('Gem.GeneratedConjureQuadruple')
require_gem('Gem.Map')
require_gem('Gem.Method')
require_gem('Gem.Path')
require_gem('Gem.System')
from Gem import create_cache, create_herd_2, create_horde_2, dump_cache_to_string, empty_herd
from Gem import print_cache, produce_conjure_by_name__V2
from Gem import produce_conjure_unique_dual, produce_conjure_unique_dual__21
from Gem import produce_conjure_quadruple__4123
from Gem import produce_conjure_unique_triple, produce_conjure_unique_triple__312
from Gem import reference_count, values_tuple_sorted_by_key, write_binary_to_path
share(
#
# Imported functions
#
'create_cache', create_cache,
'create_herd_2', create_herd_2,
'create_horde_2', create_horde_2,
'dump_cache_to_string', dump_cache_to_string,
'print_cache', print_cache,
'produce_conjure_by_name__V2', produce_conjure_by_name__V2,
'produce_conjure_unique_dual__21', produce_conjure_unique_dual__21,
'produce_conjure_unique_dual', produce_conjure_unique_dual,
'produce_conjure_unique_dual', produce_conjure_unique_dual,
'produce_conjure_quadruple__4123', produce_conjure_quadruple__4123,
'produce_conjure_unique_triple__312', produce_conjure_unique_triple__312,
'produce_conjure_unique_triple', produce_conjure_unique_triple,
'reference_count', reference_count,
'values_tuple_sorted_by_key', values_tuple_sorted_by_key,
'write_binary_to_path', write_binary_to_path,
#
# Imported Values
#
'empty_herd', empty_herd,
)
| [] |
kosovojs/wikibooster | app.py | 70a9d9d7bf41be9fa5e58d40fba216d9b6df008d | import flask
from flask import Flask
from flask import jsonify
from flask import request
from flask_cors import CORS, cross_origin
from flask import render_template
import mwoauth
import requests_oauthlib
import os
import yaml
import mwapi
from tasks.main import Tasks
from save import Save
from db import DB
from typo.fix import TypoFix
app = Flask(__name__, static_folder="./frontend/build/static", template_folder="./frontend/build")
#app = Flask(__name__)
CORS(app)
user_agent = 'WikiBooster'
__dir__ = os.path.dirname(__file__)
configFile = open(os.path.join(__dir__, 'config.yaml'))
app.config.update(yaml.safe_load(configFile))
def authenticated_session(domain = 'meta.wikimedia.org'):
if 'oauth_access_token' in flask.session:
access_token = mwoauth.AccessToken(**flask.session['oauth_access_token'])
auth = requests_oauthlib.OAuth1(client_key=app.config['CONSUMER_KEY'], client_secret=app.config['CONSUMER_SECRET'],
resource_owner_key=access_token.key, resource_owner_secret=access_token.secret)
return mwapi.Session(host='https://'+domain, auth=auth, user_agent=user_agent)
else:
return None
def getUserInfo(domain = 'meta.wikimedia.org'):
session = authenticated_session(domain)
if not session:
return None, None, {'status':'error','message':'not logged in'}
try:
userinfo = session.get(action='query',
meta='userinfo',
uiprop=['groups', 'centralids'])['query']['userinfo']
return True, session, {'status':'ok','username':userinfo['name']}
except mwapi.errors.APIError as e:
if e.code == 'mwoauth-invalid-authorization-invalid-user':
# user is viewing a batch for a wiki where they do not have a local user account
# treat as anonymous on the local wiki, but query Meta to find out if they’re a steward
return None, None, {'status':'error','message':'server error'}
else:
raise e
return None, None, {'status':'error','message':'server error'}
@app.route('/', methods=['GET'])
def index_page():
return render_template('index.html')
#http://127.0.0.1:5000/task/lvwiki/1/Helēna Mārnija
@app.route('/task/<wiki>/<name>/<page>', methods=['GET'])
def getTaskResult(wiki,name,page):
tasks = Tasks(wiki)
articleInfo = tasks.getDataForTask(name,page)
return jsonify(articleInfo)
@app.route('/testing', methods=['GET'])
def runTests():
tasks = Tasks('lvwiki')
articleInfo = tasks.runTests()
return articleInfo
@app.route('/wikis', methods=['GET'])
def listWikis():
db = DB()
wikis = db.getAvailableWikis()
return jsonify(wikis)
@app.route('/tasks/<wiki>', methods=['GET'])
def listJobs(wiki):
db = DB()
articles = db.getTasksForWiki(wiki)
return jsonify(articles)
@app.route('/task/<wiki>/<task_id>/articles', methods=['GET'])
def listArticles(wiki,task_id):
db = DB()
articles = db.get_articles_for_task(wiki,task_id)
return jsonify(articles)
#
@app.route('/typo/<wiki>', methods=['GET'])
def listTypos(wiki):
db = DB()
typos = db.getTyposForWiki(wiki)
return jsonify(typos)
@app.route('/typo/articles', methods=['GET'])
def typo_list_for_wiki():
db = DB()
wiki = 'lvwiki'
typos = db.get_typo_articles(wiki)
return jsonify(typos)
@app.route('/typo/fix/<article>', methods=['GET'])
def fix_typos(article):
db = DB()
typoFixer = TypoFix()
res = typoFixer.getData('lvwiki', article, db)
return jsonify(res)
@app.route('/rules/<wiki>', methods=['GET'])
def listRules(wiki):
db = DB()
rules = db.getRulesForWiki(wiki)
return jsonify(rules)
@app.route('/save', methods=['POST'])
def doSave():
req = request.get_json()
wiki = req['wiki']
domain = "{}.wikipedia.org".format(wiki)
userStatus, session, respFromGettingUserInfo = getUserInfo(domain)
if not userStatus:
return jsonify(respFromGettingUserInfo)
#
userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message']
job = req['job']
article = req['article']
result = req['result']
wikitext = req['wikitext']
status = req['status']
handlingSave = Save(session)
respFromSave = handlingSave.saveArticle(job,article,result,wikitext,status,userName)
return jsonify(respFromSave)
@app.route('/save_typo', methods=['POST'])
def doSaveTypo():
req = request.get_json()
wiki = req['wiki']
domain = "{}.wikipedia.org".format(wiki.replace('wiki',''))
userStatus, session, respFromGettingUserInfo = getUserInfo(domain)
if not userStatus:
return jsonify(respFromGettingUserInfo)
userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message']
active = req['active']
case = req['case']
comment = req['comment']
dumpsearch = req['dumpsearch']
minor = req['minor']
name = req['name']
regex = req['regex']
replace_with = req['replace_with']
search_for = req['search_for']
test_cases = req['test_cases']
whole = req['whole']
id = req['id']
db = DB()
typoData = db.saveTypo(active,case,comment,dumpsearch,minor,name,regex,replace_with,search_for,test_cases,whole,wiki,userName,id)
return jsonify({'status':'ok', 'info':typoData})
@app.route('/save_rule', methods=['POST'])
def saveRule():
req = request.get_json()
wiki = req['wiki']
domain = "{}.wikipedia.org".format(wiki.replace('wiki',''))
userStatus, session, respFromGettingUserInfo = getUserInfo(domain)
if not userStatus:
return jsonify(respFromGettingUserInfo)
userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message']
wiki = req['wiki']
rule_name = req['rule_name']
rule_object = req['rule_object']
rule = req['rule']
result = req['result']
id = req['id']
db = DB()
db.saveRule(id, wiki, rule_name, rule_object, rule, result)
return jsonify({'status':'ok'})
@app.route('/info', methods=['GET'])
def user_info():
userStatus, _,respFromGettingUserInfo = getUserInfo()
return jsonify(respFromGettingUserInfo)
@app.route('/login')
def login():
consumer_token = mwoauth.ConsumerToken(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET'])
redirect, request_token = mwoauth.initiate('https://meta.wikimedia.org/w/index.php', consumer_token, user_agent=user_agent)
flask.session['oauth_request_token'] = dict(zip(request_token._fields, request_token))
return flask.redirect(redirect)
@app.route('/oauth-callback')
def oauth_callback():
consumer_token = mwoauth.ConsumerToken(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET'])
request_token = mwoauth.RequestToken(**flask.session.pop('oauth_request_token'))
access_token = mwoauth.complete('https://meta.wikimedia.org/w/index.php', consumer_token, request_token, flask.request.query_string, user_agent=user_agent)
flask.session['oauth_access_token'] = dict(zip(access_token._fields, access_token))
return flask.redirect(flask.url_for('index_page'))
@app.route('/logout')
def logout():
"""Log the user out by clearing their session."""
flask.session.clear()
return flask.redirect(flask.url_for('index_page'))
if __name__ == '__main__':
app.run(debug=True) | [((20, 6, 20, 98), 'flask.Flask', 'Flask', (), '', False, 'from flask import Flask\n'), ((22, 0, 22, 9), 'flask_cors.CORS', 'CORS', ({(22, 5, 22, 8): 'app'}, {}), '(app)', False, 'from flask_cors import CORS, cross_origin\n'), ((26, 10, 26, 35), 'os.path.dirname', 'os.path.dirname', ({(26, 26, 26, 34): '__file__'}, {}), '(__file__)', False, 'import os\n'), ((28, 18, 28, 54), 'os.path.join', 'os.path.join', ({(28, 31, 28, 38): '__dir__', (28, 40, 28, 53): '"""config.yaml"""'}, {}), "(__dir__, 'config.yaml')", False, 'import os\n'), ((29, 18, 29, 44), 'yaml.safe_load', 'yaml.safe_load', ({(29, 33, 29, 43): 'configFile'}, {}), '(configFile)', False, 'import yaml\n'), ((66, 8, 66, 37), 'flask.render_template', 'render_template', ({(66, 24, 66, 36): '"""index.html"""'}, {}), "('index.html')", False, 'from flask import render_template\n'), ((71, 9, 71, 20), 'tasks.main.Tasks', 'Tasks', ({(71, 15, 71, 19): 'wiki'}, {}), '(wiki)', False, 'from tasks.main import Tasks\n'), 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db import DB\n'), ((108, 8, 108, 22), 'flask.jsonify', 'jsonify', ({(108, 16, 108, 21): 'typos'}, {}), '(typos)', False, 'from flask import jsonify\n'), ((113, 6, 113, 10), 'db.DB', 'DB', ({}, {}), '()', False, 'from db import DB\n'), ((116, 8, 116, 22), 'flask.jsonify', 'jsonify', ({(116, 16, 116, 21): 'typos'}, {}), '(typos)', False, 'from flask import jsonify\n'), ((120, 6, 120, 10), 'db.DB', 'DB', ({}, {}), '()', False, 'from db import DB\n'), ((121, 13, 121, 22), 'typo.fix.TypoFix', 'TypoFix', ({}, {}), '()', False, 'from typo.fix import TypoFix\n'), ((125, 8, 125, 20), 'flask.jsonify', 'jsonify', ({(125, 16, 125, 19): 'res'}, {}), '(res)', False, 'from flask import jsonify\n'), ((129, 6, 129, 10), 'db.DB', 'DB', ({}, {}), '()', False, 'from db import DB\n'), ((131, 8, 131, 22), 'flask.jsonify', 'jsonify', ({(131, 16, 131, 21): 'rules'}, {}), '(rules)', False, 'from flask import jsonify\n'), ((135, 7, 135, 25), 'flask.request.get_json', 'request.get_json', ({}, {}), '()', False, 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{}), "({'status': 'ok'})", False, 'from flask import jsonify\n'), ((214, 8, 214, 40), 'flask.jsonify', 'jsonify', ({(214, 16, 214, 39): 'respFromGettingUserInfo'}, {}), '(respFromGettingUserInfo)', False, 'from flask import jsonify\n'), ((218, 18, 218, 98), 'mwoauth.ConsumerToken', 'mwoauth.ConsumerToken', ({(218, 40, 218, 66): "app.config['CONSUMER_KEY']", (218, 68, 218, 97): "app.config['CONSUMER_SECRET']"}, {}), "(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET']\n )", False, 'import mwoauth\n'), ((219, 27, 219, 124), 'mwoauth.initiate', 'mwoauth.initiate', (), '', False, 'import mwoauth\n'), ((221, 8, 221, 32), 'flask.redirect', 'flask.redirect', ({(221, 23, 221, 31): 'redirect'}, {}), '(redirect)', False, 'import flask\n'), ((225, 18, 225, 98), 'mwoauth.ConsumerToken', 'mwoauth.ConsumerToken', ({(225, 40, 225, 66): "app.config['CONSUMER_KEY']", (225, 68, 225, 97): "app.config['CONSUMER_SECRET']"}, {}), "(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET']\n )", False, 'import mwoauth\n'), ((227, 16, 227, 156), 'mwoauth.complete', 'mwoauth.complete', (), '', False, 'import mwoauth\n'), ((234, 1, 234, 22), 'flask.session.clear', 'flask.session.clear', ({}, {}), '()', False, 'import flask\n'), ((33, 17, 33, 75), 'mwoauth.AccessToken', 'mwoauth.AccessToken', ({}, {}), "(**flask.session['oauth_access_token'])", False, 'import mwoauth\n'), ((34, 9, 35, 89), 'requests_oauthlib.OAuth1', 'requests_oauthlib.OAuth1', (), '', False, 'import requests_oauthlib\n'), ((36, 9, 36, 80), 'mwapi.Session', 'mwapi.Session', (), '', False, 'import mwapi\n'), ((141, 9, 141, 41), 'flask.jsonify', 'jsonify', ({(141, 17, 141, 40): 'respFromGettingUserInfo'}, {}), '(respFromGettingUserInfo)', False, 'from flask import jsonify\n'), ((164, 9, 164, 41), 'flask.jsonify', 'jsonify', ({(164, 17, 164, 40): 'respFromGettingUserInfo'}, {}), '(respFromGettingUserInfo)', False, 'from flask import jsonify\n'), ((194, 9, 194, 41), 'flask.jsonify', 'jsonify', ({(194, 17, 194, 40): 'respFromGettingUserInfo'}, {}), '(respFromGettingUserInfo)', False, 'from flask import jsonify\n'), ((229, 23, 229, 50), 'flask.url_for', 'flask.url_for', ({(229, 37, 229, 49): '"""index_page"""'}, {}), "('index_page')", False, 'import flask\n'), ((235, 23, 235, 50), 'flask.url_for', 'flask.url_for', ({(235, 37, 235, 49): '"""index_page"""'}, {}), "('index_page')", False, 'import flask\n'), ((226, 40, 226, 80), 'flask.session.pop', 'flask.session.pop', ({(226, 58, 226, 79): '"""oauth_request_token"""'}, {}), "('oauth_request_token')", False, 'import flask\n')] |
shelleyyyyu/few_shot | pre_embed.py | 0fe54444e820fe3201927e6363682913b6d61028 | import numpy as np
from collections import defaultdict, Counter
import random
import json
from tqdm import tqdm
def transX(dataset):
rel2id = json.load(open(dataset + '/relation2ids'))
ent2id = json.load(open(dataset + '/ent2ids'))
with open('../Fast-TransX/' + dataset + '_base/entity2id.txt', 'w') as g1:
num_ents = len(ent2id.keys())
g1.write(str(num_ents) + '\n')
for k, v in ent2id.items():
g1.write(k + '\t' + str(v) + '\n')
with open('../Fast-TransX/' + dataset + '_base/relation2id.txt', 'w') as g1:
num_rels = len(rel2id.keys())
g1.write(str(num_rels) + '\n')
for k, v in rel2id.items():
g1.write(k + '\t' + str(v) + '\n')
file_name = dataset + '/path_graph'
train_triples = []
with open(file_name) as f:
lines = f.readlines()
for line in tqdm(lines):
e1 = line.split('\t')[0]
e2 = line.rstrip().split('\t')[2]
rel = line.split('\t')[1]
train_triples.append([e1,rel,e2])
train_triples.append([e2,rel+'_inv',e1])
with open('../Fast-TransX/' + dataset + '_base/train2id.txt', 'w') as g3:
num_triples = len(train_triples)
g3.write(str(num_triples) + '\n')
for triple in train_triples:
e1, rel, e2 = triple
g3.write(str(ent2id[e1]) + '\t' + str(ent2id[e2]) + '\t' + str(rel2id[rel]) + '\n')
if __name__ == '__main__':
transX('Wiki') | [((30, 20, 30, 31), 'tqdm.tqdm', 'tqdm', ({(30, 25, 30, 30): 'lines'}, {}), '(lines)', False, 'from tqdm import tqdm\n')] |
Xinverse/BOTC-Bot | botc/gamemodes/troublebrewing/FortuneTeller.py | 1932c649c81a5a1eab735d7abdee0761c2853940 | """Contains the Fortune Teller Character class"""
import json
import random
import discord
import datetime
from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, \
RecurringAction, Category, StatusList
from botc.BOTCUtils import GameLogic
from ._utils import TroubleBrewing, TBRole
import globvars
with open('botc/gamemodes/troublebrewing/character_text.json') as json_file:
character_text = json.load(json_file)[TBRole.fortuneteller.value.lower()]
with open('botutils/bot_text.json') as json_file:
bot_text = json.load(json_file)
butterfly = bot_text["esthetics"]["butterfly"]
with open('botc/game_text.json') as json_file:
strings = json.load(json_file)
fortune_teller_nightly = strings["gameplay"]["fortune_teller_nightly"]
copyrights_str = strings["misc"]["copyrights"]
yes = strings["gameplay"]["yes"]
no = strings["gameplay"]["no"]
good_link = strings["images"]["good"]
evil_link = strings["images"]["evil"]
class FortuneTeller(Townsfolk, TroubleBrewing, Character, RecurringAction):
"""Fortune Teller: Each night, choose 2 players: you learn if either is a Demon.
There is 1 good player that registers falsely to you.
===== FORTUNE TELLER =====
true_self = fortune teller
ego_self = fortune teller
social_self = fortune teller
commands:
- read <player> and <player>
initialize setup? -> NO
initialize role? -> YES
----- First night
START:
override first night instruction? -> YES # default is to send instruction string only
=> Send query for "read" command
----- Regular night
START:
override regular night instruction? -> YES # default is to send nothing
=> Send query for "read" command
"""
def __init__(self):
Character.__init__(self)
TroubleBrewing.__init__(self)
Townsfolk.__init__(self)
self._desc_string = character_text["description"]
self._examp_string = character_text["examples"]
self._instr_string = character_text["instruction"]
self._lore_string = character_text["lore"]
self._brief_string = character_text["brief"]
self._action = character_text["action"]
self._art_link = "https://bloodontheclocktower.com/wiki/images/3/3a/Fortune_Teller_Token.png"
self._art_link_cropped = "https://imgur.com/23ZXb1y.png"
self._wiki_link = "https://bloodontheclocktower.com/wiki/Fortune_Teller"
self._role_enum = TBRole.fortuneteller
self._emoji = "<:tbfortuneteller:739317350733578280>"
def create_n1_instr_str(self):
"""Create the instruction field on the opening dm card"""
# First line is the character instruction string
msg = f"{self.emoji} {self.instruction}"
addendum = character_text["n1_addendum"]
# Some characters have a line of addendum
if addendum:
with open("botutils/bot_text.json") as json_file:
bot_text = json.load(json_file)
scroll_emoji = bot_text["esthetics"]["scroll"]
msg += f"\n{scroll_emoji} {addendum}"
return msg
def add_action_field_n1(self, embed_obj):
"""Send the stats list n1"""
msg = self.action
msg += globvars.master_state.game.create_sitting_order_stats_string()
embed_obj.add_field(name = butterfly + " **「 Your Action 」**", value = msg, inline = False)
return embed_obj
def exec_init_role(self, setup):
"""Assign one of the townsfolks or outsiders as a red herring"""
possibilities = setup.townsfolks + setup.outsiders
chosen = random.choice(possibilities)
chosen.add_status_effect(RedHerring(Storyteller(), chosen))
globvars.logging.info(f">>> Fortune Teller [exec_init_role] Set red herring to {str(chosen)}")
def has_finished_night_action(self, player):
"""Return True if fortune teller has submitted the read action"""
if player.is_alive():
current_phase_id = globvars.master_state.game._chrono.phase_id
received_action = player.action_grid.retrieve_an_action(current_phase_id)
return received_action is not None and received_action.action_type == ActionTypes.read
return True
@GameLogic.requires_two_targets
@GameLogic.requires_different_targets
@GameLogic.changes_not_allowed
async def register_read(self, player, targets):
"""Read command"""
# Must be 2 targets
assert len(targets) == 2, "Received a number of targets different than 2 for fortune teller 'read'"
action = Action(player, targets, ActionTypes.read, globvars.master_state.game._chrono.phase_id)
player.action_grid.register_an_action(action, globvars.master_state.game._chrono.phase_id)
msg = butterfly + " " + character_text["feedback"].format(targets[0].game_nametag, targets[1].game_nametag)
await player.user.send(msg)
async def exec_read(self, fortune_teller_player, read_player_1, read_player_2):
"""Execute the read action (night ability interaction)"""
if fortune_teller_player.is_alive():
# Correct info
if not fortune_teller_player.is_droisoned():
response = read_player_1.role.social_self.category == Category.demon or \
read_player_2.role.social_self.category == Category.demon or \
read_player_1.has_status_effect(StatusList.red_herring) or \
read_player_2.has_status_effect(StatusList.red_herring)
# Droisoned info
else:
response = random.choice((True, False))
reply = yes if response else no
link = evil_link if response else good_link
recipient = fortune_teller_player.user
msg = f"***{recipient.name}#{recipient.discriminator}***, the **{self.name}**:"
msg += "\n"
msg += self.emoji + " " + self.instruction
msg += "\n"
msg += fortune_teller_nightly.format(reply)
embed = discord.Embed(description = msg)
embed.set_thumbnail(url = link)
embed.set_footer(text = copyrights_str)
embed.timestamp = datetime.datetime.utcnow()
try:
await recipient.send(embed = embed)
except discord.Forbidden:
pass
# If the fortune teller player is dead, then nothing is sent to them
else:
pass
async def process_night_ability(self, player):
"""Process night actions for the fortune teller character.
@player : the Fortune Teller player (Player object)
"""
phase = globvars.master_state.game._chrono.phase_id
action = player.action_grid.retrieve_an_action(phase)
# The Fortune teller has submitted an action. We call the execution function immediately
if action:
assert action.action_type == ActionTypes.read, f"Wrong action type {action} in fortune teller"
targets = action.target_player
read_player_1 = targets[0]
read_player_2 = targets[1]
await self.exec_read(player, read_player_1, read_player_2)
# The fortune teller has not submitted an action. We will not randomize the action since
# the reading ability is a "priviledged" ability
else:
pass
| [((17, 15, 17, 35), 'json.load', 'json.load', ({(17, 25, 17, 34): 'json_file'}, {}), '(json_file)', False, 'import json\n'), ((21, 14, 21, 34), 'json.load', 'json.load', ({(21, 24, 21, 33): 'json_file'}, {}), '(json_file)', False, 'import json\n'), ((14, 21, 14, 41), 'json.load', 'json.load', ({(14, 31, 14, 40): 'json_file'}, {}), '(json_file)', False, 'import json\n'), ((59, 8, 59, 32), 'botc.Character.__init__', 'Character.__init__', ({(59, 27, 59, 31): 'self'}, {}), '(self)', False, 'from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, RecurringAction, Category, StatusList\n'), ((61, 8, 61, 32), 'botc.Townsfolk.__init__', 'Townsfolk.__init__', ({(61, 27, 61, 31): 'self'}, {}), '(self)', False, 'from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, RecurringAction, Category, StatusList\n'), ((97, 15, 97, 77), 'globvars.master_state.game.create_sitting_order_stats_string', 'globvars.master_state.game.create_sitting_order_stats_string', ({}, {}), '()', False, 'import globvars\n'), ((105, 17, 105, 45), 'random.choice', 'random.choice', ({(105, 31, 105, 44): 'possibilities'}, {}), '(possibilities)', False, 'import random\n'), ((126, 17, 126, 103), 'botc.Action', 'Action', ({(126, 24, 126, 30): 'player', (126, 32, 126, 39): 'targets', (126, 41, 126, 57): 'ActionTypes.read', (126, 59, 126, 102): 'globvars.master_state.game._chrono.phase_id'}, {}), '(player, targets, ActionTypes.read, globvars.master_state.game.\n _chrono.phase_id)', False, 'from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, RecurringAction, Category, StatusList\n'), ((155, 20, 155, 52), 'discord.Embed', 'discord.Embed', (), '', False, 'import discord\n'), ((158, 30, 158, 56), 'datetime.datetime.utcnow', 'datetime.datetime.utcnow', ({}, {}), '()', False, 'import datetime\n'), ((87, 27, 87, 47), 'json.load', 'json.load', ({(87, 37, 87, 46): 'json_file'}, {}), '(json_file)', False, 'import json\n'), ((106, 44, 106, 57), 'botc.Storyteller', 'Storyteller', ({}, {}), '()', False, 'from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, RecurringAction, Category, StatusList\n'), ((143, 27, 143, 55), 'random.choice', 'random.choice', ({(143, 41, 143, 54): '(True, False)'}, {}), '((True, False))', False, 'import random\n')] |
bjuvensjo/schmetterling | src/schmetterling/build/tests/test_maven.py | 0cdbfe4f379a081d9d4711dd21866b90983365cf | from unittest.mock import call, MagicMock, patch
from schmetterling.build.maven import build_multi_modules
from schmetterling.build.maven import create_build_result
from schmetterling.build.maven import create_command
from schmetterling.build.maven import create_multi_modules
from schmetterling.build.maven import create_state
from schmetterling.build.maven import get_maven_infos
from schmetterling.build.maven import get_maven_repos
from schmetterling.build.maven import get_multi_modules
from schmetterling.build.state import BuildState, Build
from schmetterling.setup.state import Repo
def test_build_multi_modules():
mm = [
{
'updated': 'updated1',
'pom_dir': 'pom_dir1',
'coordinates': 'coordinates1'
},
{
'updated': 'updated2',
'pom_dir': 'pom_dir2',
'coordinates': 'coordinates2'
},
]
with patch(
'schmetterling.build.maven.create_command',
return_value='create_command') as m_create_command, patch(
'schmetterling.build.maven.run_command') as m_run_command, patch(
'schmetterling.build.maven.create_build_result',
return_value=[['success_coordinates'], [
'failure_coordinates'
]]) as m_create_build_result:
assert (
['success_coordinates', 'success_coordinates'],
['failure_coordinates', 'failure_coordinates'],
) == build_multi_modules(mm, 'repository_dir', 'settings_file', 'logback_file')
assert [
call('updated1', 'pom_dir1/mvn.log', 'repository_dir', 'settings_file', 'logback_file'),
call('updated2', 'pom_dir2/mvn.log', 'repository_dir', 'settings_file', 'logback_file')
] == m_create_command.mock_calls
assert [
call('create_command', cwd='pom_dir1'),
call('create_command', cwd='pom_dir2')
] == m_run_command.mock_calls
assert [
call('coordinates1', 'updated1', 'pom_dir1/mvn.log'),
call('coordinates2', 'updated2', 'pom_dir2/mvn.log')
] == m_create_build_result.mock_calls
def test_create_command():
assert str('mvn -Dmaven.repo.local=repository '
'-s settings.xml '
'-DcreateChecksum=true '
'-Dfile.encoding=UTF-8 '
'-Dsun.jnu.encoding=UTF-8 '
'-Dlogback.configurationFile=logback.xml '
'-B -amd -pl mygroup:app.admin,mygroup:app.sign '
'clean install javadoc:jar source:jar '
'--fail-at-end | tee mvn.log') == create_command(
[{
'artifact_id': 'app.admin',
'group_id': 'mygroup',
}, {
'artifact_id': 'app.sign',
'group_id': 'mygroup',
}], 'mvn.log', 'repository', 'settings.xml', 'logback.xml')
@patch(
'schmetterling.build.maven.get_summary',
return_value=(['mygroup:app.admin'], ['app.sign']))
def test_create_build_result(mock_get_summary):
assert (
[
{
'artifact_id': 'app.admin',
'group_id': 'mygroup',
},
],
[
{
'artifact_id': 'app.sign',
'group_id': 'mygroup',
},
{
'artifact_id': 'pipeline.env',
'group_id': 'mygroup',
},
],
) == create_build_result(
[
{
'artifact_id': 'app.admin',
'group_id': 'mygroup',
},
{
'artifact_id': 'app.sign',
'group_id': 'mygroup',
},
{
'artifact_id': 'pipeline.env',
'group_id': 'mygroup',
},
{
'artifact_id': 'xml.ws',
'group_id': 'mygroup',
},
],
[
{
'artifact_id': 'app.admin',
'group_id': 'mygroup',
},
{
'artifact_id': 'app.sign',
'group_id': 'mygroup',
},
{
'artifact_id': 'pipeline.env',
'group_id': 'mygroup',
},
],
'mvn.log',
)
def test_create_multi_modules():
with patch('schmetterling.build.maven.makedirs') as m, patch(
'schmetterling.build.maven.open') as o:
f = MagicMock()
o.return_value = MagicMock(__enter__=MagicMock(return_value=f))
create_multi_modules([
{
'pom_dir': 'pd1',
'pom_content': 'pc1'
},
{
'pom_dir': 'pd2',
'pom_content': 'pc2'
},
])
assert [call('pd1', exist_ok=True),
call('pd2', exist_ok=True)] == m.mock_calls
assert [call.write('pc1'), call.write('pc2')] == f.mock_calls
def test_create_state():
state = BuildState('schmetterling.build.maven',
[
Build('mygroup', 'app.admin', '0.0.1-SNAPSHOT', 'app.admin',
Build.SUCCESS, 1),
Build('mygroup', 'pipeline-apache-proxy', '1.0.0-SNAPSHOT',
'pipeline-apache-proxy', Build.FAILURE, 1),
])
assert state == create_state(
[],
[{
'pom_path': 'app.admin/pom.xml',
'artifact_id': 'app.admin',
'group_id': 'mygroup',
'version': '0.0.1-SNAPSHOT',
'packaging': 'jar'
}],
[{
'pom_path': 'pipeline-apache-proxy/pom.xml',
'artifact_id': 'pipeline-apache-proxy',
'group_id': 'mygroup',
'version': '1.0.0-SNAPSHOT',
'packaging': 'jar'
}],
1,
)
def test_get_maven_info():
with patch('schmetterling.build.maven.get_pom_info', side_effect=lambda x: x):
repos = [
MagicMock(status=Repo.STATUS_UPDATED, path='path1'),
MagicMock(status=Repo.STATUS_UNCHANGED, path='path2'),
]
assert [(True, 'path1/pom.xml'),
(False, 'path2/pom.xml')] == get_maven_infos(repos)
def test_get_maven_repos():
with patch('schmetterling.build.maven.isinstance', return_value=True):
with patch('schmetterling.build.maven.exists', side_effect=[False, True]):
m = MagicMock(path='pom_repo', return_value='pom_repo')
state = [MagicMock(repos=[
MagicMock(path='non_pom_repo'),
m,
])]
assert [m] == get_maven_repos(state)
def test_get_multi_modules():
with patch('schmetterling.build.maven.get_pom', return_value='pom_content'):
assert [] == get_multi_modules([(False, {})], 'build_dir')
assert [{
'coordinates': [{}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/jar-modules',
'updated': [{}]
}] == get_multi_modules([(True, {})], 'build_dir')
assert [{
'coordinates': [{
'packaging': 'jar'
}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/jar-modules',
'updated': [{
'packaging': 'jar'
}]
}] == get_multi_modules([(True, {
'packaging': 'jar'
})], 'build_dir')
assert [{
'coordinates': [{
'artifact_id': 'super-pom',
'packaging': 'pom'
}],
'pom_content':
'pom_content',
'pom_dir':
'build_dir/super-pom-modules',
'updated': [{
'artifact_id': 'super-pom',
'packaging': 'pom'
}]
}] == get_multi_modules([(True, {
'artifact_id': 'super-pom',
'packaging': 'pom'
})], 'build_dir')
assert [{
'coordinates': [{
'artifact_id': 'pom',
'packaging': 'pom'
}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/pom-pom-modules',
'updated': [{
'artifact_id': 'pom',
'packaging': 'pom'
}]
}] == get_multi_modules([(True, {
'artifact_id': 'pom',
'packaging': 'pom'
})], 'build_dir')
assert [{
'coordinates': [{
'artifact_id': 'x',
'packaging': 'x'
}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/other-modules',
'updated': [{
'artifact_id': 'x',
'packaging': 'x'
}]
}] == get_multi_modules([(True, {
'artifact_id': 'x',
'packaging': 'x'
})], 'build_dir')
assert [{
'coordinates': [{
'artifact_id': 'war',
'packaging': 'war'
}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/war-modules',
'updated': [{
'artifact_id': 'war',
'packaging': 'war'
}]
}] == get_multi_modules([(True, {
'artifact_id': 'war',
'packaging': 'war'
})], 'build_dir')
assert [{
'coordinates': [{
'artifact_id': 'jar1',
'packaging': 'jar'
}, {
'artifact_id': 'jar2'
}, {
'artifact_id': 'jar3'
}],
'pom_content':
'pom_content',
'pom_dir':
'build_dir/jar-modules',
'updated': [{
'artifact_id': 'jar1',
'packaging': 'jar'
}, {
'artifact_id': 'jar2'
}]
}, {
'coordinates': [{
'artifact_id': 'war',
'packaging': 'war'
}],
'pom_content': 'pom_content',
'pom_dir': 'build_dir/war-modules',
'updated': [{
'artifact_id': 'war',
'packaging': 'war'
}]
}] == get_multi_modules([(True, {
'artifact_id': 'jar1',
'packaging': 'jar'
}), (True, {
'artifact_id': 'jar2'
}), (False, {
'artifact_id': 'jar3'
}), (True, {
'artifact_id': 'war',
'packaging': 'war'
})], 'build_dir')
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'MagicMock', (), '', False, 'from unittest.mock import call, MagicMock, patch\n')] |
FdelMazo/7540rw-Algo1 | Copados y Clases/Mastermind_DEBUG.py | 8900604873195df9e902ead6bcb67723a8b654c8 | #Sacar las lineas con DEBUG para que el juego funcione
import random
DIGITOS = 4
def mastermind():
"""Funcion principal del juego Mastermind"""
print("Bienvenido al Mastermind!")
print("Instrucciones: Tenes que adivinar un codigo de {} digitos distintos. Tu cantidad de aciertos son los numeros que estan correctamente posicionados, tu cantidad de coincidencias son los numeros bien elegidos pero mal posicionados. Suerte!".format(DIGITOS))
codigo = elegir_codigo()
intentos = 1
propuesta = input("Que codigo propones? (o pone 'Me retiro') ")
retirarse = "Me retiro"
while propuesta != codigo and propuesta != retirarse:
intentos+=1
aciertos, coincidencias = analizar_propuesta(propuesta, codigo)
print ("Tu propuesta ({}) tiene {} aciertos y {} coincidencias.".format(propuesta,aciertos,coincidencias))
propuesta = input("Propone otro codigo: ")
if propuesta == retirarse:
print ("El codigo era: {}".format(codigo))
else:
print ("Ganaste! Ganaste en {} intentos".format(intentos))
def elegir_codigo():
"""Elige un codigo de DIGITOS digitos al azar"""
digitos= ("0","1","2","3","4","5","6","7","8","9")
codigo = ""
for i in range(DIGITOS):
candidato = random.choice(digitos)
print("[DEBUG] candidato:", candidato)
while candidato in codigo:
candidato = random.choice(digitos)
codigo = codigo + candidato
print("[DEBUG] el codigo va siendo", codigo)
return codigo
def analizar_propuesta(propuesta, codigo):
"""Determina aciertos y coincidencias"""
aciertos = 0
coincidencias = 0
for i in range(DIGITOS):
if propuesta[i] == codigo[i]:
aciertos += 1
elif propuesta[i] in codigo:
coincidencias += 1
return aciertos,coincidencias
mastermind() | [((29, 14, 29, 36), 'random.choice', 'random.choice', ({(29, 28, 29, 35): 'digitos'}, {}), '(digitos)', False, 'import random\n'), ((32, 15, 32, 37), 'random.choice', 'random.choice', ({(32, 29, 32, 36): 'digitos'}, {}), '(digitos)', False, 'import random\n')] |
ovnicraft/runa | setup.py | 4834b7467314c51c3e8e010b47a10bdfae597a5b | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""The setup script."""
from setuptools import setup, find_packages
with open("README.rst") as readme_file:
readme = readme_file.read()
with open("HISTORY.rst") as history_file:
history = history_file.read()
requirements = ["Click>=6.0", "suds2==0.7.1"]
setup_requirements = [
# TODO(ovnicraft): put setup requirements (distutils extensions, etc.) here
]
test_requirements = [
# TODO: put package test requirements here
]
setup(
name="runa",
version="0.2.10",
description="Librería para uso de WS del Bus Gubernamental de Ecuador",
long_description=readme + "\n\n" + history,
author="Cristian Salamea",
author_email="[email protected]",
url="https://github.com/ovnicraft/runa",
packages=find_packages(include=["runa"]),
entry_points={"console_scripts": ["runa=runa.cli:main"]},
include_package_data=True,
install_requires=requirements,
license="MIT license",
zip_safe=False,
keywords="runa webservices ecuador bgs",
classifiers=[
"Development Status :: 3 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Natural Language :: English",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
],
test_suite="tests",
tests_require=test_requirements,
setup_requires=setup_requirements,
)
| [((32, 13, 32, 44), 'setuptools.find_packages', 'find_packages', (), '', False, 'from setuptools import setup, find_packages\n')] |
RichardA1/Adafruit_Learning_System_Guides | PyPortal_User_Interface/code.py | 7d06d8a126f357a431384c3af73339cb46f44c19 | import time
import board
import displayio
import busio
from analogio import AnalogIn
import neopixel
import adafruit_adt7410
from adafruit_bitmap_font import bitmap_font
from adafruit_display_text.label import Label
from adafruit_button import Button
import adafruit_touchscreen
from adafruit_pyportal import PyPortal
# ------------- Inputs and Outputs Setup ------------- #
# init. the temperature sensor
i2c_bus = busio.I2C(board.SCL, board.SDA)
adt = adafruit_adt7410.ADT7410(i2c_bus, address=0x48)
adt.high_resolution = True
# init. the light sensor
light_sensor = AnalogIn(board.LIGHT)
pixel = neopixel.NeoPixel(board.NEOPIXEL, 1, brightness=1)
WHITE = 0xffffff
RED = 0xff0000
YELLOW = 0xffff00
GREEN = 0x00ff00
BLUE = 0x0000ff
PURPLE = 0xff00ff
BLACK = 0x000000
# ---------- Sound Effects ------------- #
soundDemo = '/sounds/sound.wav'
soundBeep = '/sounds/beep.wav'
soundTab = '/sounds/tab.wav'
# ------------- Other Helper Functions------------- #
# Helper for cycling through a number set of 1 to x.
def numberUP(num, max_val):
num += 1
if num <= max_val:
return num
else:
return 1
# ------------- Screen Setup ------------- #
pyportal = PyPortal()
display = board.DISPLAY
display.rotation = 270
# Backlight function
# Value between 0 and 1 where 0 is OFF, 0.5 is 50% and 1 is 100% brightness.
def set_backlight(val):
val = max(0, min(1.0, val))
board.DISPLAY.auto_brightness = False
board.DISPLAY.brightness = val
# Set the Backlight
set_backlight(0.3)
# Touchscreen setup
# ------Rotate 270:
screen_width = 240
screen_height = 320
ts = adafruit_touchscreen.Touchscreen(board.TOUCH_YD, board.TOUCH_YU,
board.TOUCH_XR, board.TOUCH_XL,
calibration=((5200, 59000),
(5800, 57000)),
size=(screen_width, screen_height))
# ------------- Display Groups ------------- #
splash = displayio.Group(max_size=15) # The Main Display Group
view1 = displayio.Group(max_size=15) # Group for View 1 objects
view2 = displayio.Group(max_size=15) # Group for View 2 objects
view3 = displayio.Group(max_size=15) # Group for View 3 objects
def hideLayer(hide_target):
try:
splash.remove(hide_target)
except ValueError:
pass
def showLayer(show_target):
try:
time.sleep(0.1)
splash.append(show_target)
except ValueError:
pass
# ------------- Setup for Images ------------- #
# Display an image until the loop starts
pyportal.set_background('/images/loading.bmp')
bg_group = displayio.Group(max_size=1)
splash.append(bg_group)
icon_group = displayio.Group(max_size=1)
icon_group.x = 180
icon_group.y = 120
icon_group.scale = 1
view2.append(icon_group)
# This will handel switching Images and Icons
def set_image(group, filename):
"""Set the image file for a given goup for display.
This is most useful for Icons or image slideshows.
:param group: The chosen group
:param filename: The filename of the chosen image
"""
print("Set image to ", filename)
if group:
group.pop()
if not filename:
return # we're done, no icon desired
image_file = open(filename, "rb")
image = displayio.OnDiskBitmap(image_file)
try:
image_sprite = displayio.TileGrid(image, pixel_shader=displayio.ColorConverter())
except TypeError:
image_sprite = displayio.TileGrid(image, pixel_shader=displayio.ColorConverter(),
position=(0, 0))
group.append(image_sprite)
set_image(bg_group, "/images/BGimage.bmp")
# ---------- Text Boxes ------------- #
# Set the font and preload letters
font = bitmap_font.load_font("/fonts/Helvetica-Bold-16.bdf")
font.load_glyphs(b'abcdefghjiklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890- ()')
# Default Label styling:
TABS_X = 5
TABS_Y = 50
# Text Label Objects
feed1_label = Label(font, text="Text Wondow 1", color=0xE39300, max_glyphs=200)
feed1_label.x = TABS_X
feed1_label.y = TABS_Y
view1.append(feed1_label)
feed2_label = Label(font, text="Text Wondow 2", color=0xFFFFFF, max_glyphs=200)
feed2_label.x = TABS_X
feed2_label.y = TABS_Y
view2.append(feed2_label)
sensors_label = Label(font, text="Data View", color=0x03AD31, max_glyphs=200)
sensors_label.x = TABS_X
sensors_label.y = TABS_Y
view3.append(sensors_label)
sensor_data = Label(font, text="Data View", color=0x03AD31, max_glyphs=100)
sensor_data.x = TABS_X+15
sensor_data.y = 170
view3.append(sensor_data)
text_hight = Label(font, text="M", color=0x03AD31, max_glyphs=10)
# return a reformatted string with word wrapping using PyPortal.wrap_nicely
def text_box(target, top, string, max_chars):
text = pyportal.wrap_nicely(string, max_chars)
new_text = ""
test = ""
for w in text:
new_text += '\n'+w
test += 'M\n'
text_hight.text = test # Odd things happen without this
glyph_box = text_hight.bounding_box
target.text = "" # Odd things happen without this
target.y = int(glyph_box[3]/2)+top
target.text = new_text
# ---------- Display Buttons ------------- #
# Default button styling:
BUTTON_HEIGHT = 40
BUTTON_WIDTH = 80
# We want three buttons across the top of the screen
TAPS_HEIGHT = 40
TAPS_WIDTH = int(screen_width/3)
TAPS_Y = 0
# We want two big buttons at the bottom of the screen
BIG_BUTTON_HEIGHT = int(screen_height/3.2)
BIG_BUTTON_WIDTH = int(screen_width/2)
BIG_BUTTON_Y = int(screen_height-BIG_BUTTON_HEIGHT)
# This group will make it easy for us to read a button press later.
buttons = []
# Main User Interface Buttons
button_view1 = Button(x=0, y=0,
width=TAPS_WIDTH, height=TAPS_HEIGHT,
label="View1", label_font=font, label_color=0xff7e00,
fill_color=0x5c5b5c, outline_color=0x767676,
selected_fill=0x1a1a1a, selected_outline=0x2e2e2e,
selected_label=0x525252)
buttons.append(button_view1) # adding this button to the buttons group
button_view2 = Button(x=TAPS_WIDTH, y=0,
width=TAPS_WIDTH, height=TAPS_HEIGHT,
label="View2", label_font=font, label_color=0xff7e00,
fill_color=0x5c5b5c, outline_color=0x767676,
selected_fill=0x1a1a1a, selected_outline=0x2e2e2e,
selected_label=0x525252)
buttons.append(button_view2) # adding this button to the buttons group
button_view3 = Button(x=TAPS_WIDTH*2, y=0,
width=TAPS_WIDTH, height=TAPS_HEIGHT,
label="View3", label_font=font, label_color=0xff7e00,
fill_color=0x5c5b5c, outline_color=0x767676,
selected_fill=0x1a1a1a, selected_outline=0x2e2e2e,
selected_label=0x525252)
buttons.append(button_view3) # adding this button to the buttons group
button_switch = Button(x=0, y=BIG_BUTTON_Y,
width=BIG_BUTTON_WIDTH, height=BIG_BUTTON_HEIGHT,
label="Switch", label_font=font, label_color=0xff7e00,
fill_color=0x5c5b5c, outline_color=0x767676,
selected_fill=0x1a1a1a, selected_outline=0x2e2e2e,
selected_label=0x525252)
buttons.append(button_switch) # adding this button to the buttons group
button_2 = Button(x=BIG_BUTTON_WIDTH, y=BIG_BUTTON_Y,
width=BIG_BUTTON_WIDTH, height=BIG_BUTTON_HEIGHT,
label="Button", label_font=font, label_color=0xff7e00,
fill_color=0x5c5b5c, outline_color=0x767676,
selected_fill=0x1a1a1a, selected_outline=0x2e2e2e,
selected_label=0x525252)
buttons.append(button_2) # adding this button to the buttons group
# Add all of the main buttons to the spalsh Group
for b in buttons:
splash.append(b.group)
# Make a button to change the icon image on view2
button_icon = Button(x=150, y=60,
width=BUTTON_WIDTH, height=BUTTON_HEIGHT,
label="Icon", label_font=font, label_color=0xffffff,
fill_color=0x8900ff, outline_color=0xbc55fd,
selected_fill=0x5a5a5a, selected_outline=0xff6600,
selected_label=0x525252, style=Button.ROUNDRECT)
buttons.append(button_icon) # adding this button to the buttons group
# Add this button to view2 Group
view2.append(button_icon.group)
# Make a button to play a sound on view2
button_sound = Button(x=150, y=170,
width=BUTTON_WIDTH, height=BUTTON_HEIGHT,
label="Sound", label_font=font, label_color=0xffffff,
fill_color=0x8900ff, outline_color=0xbc55fd,
selected_fill=0x5a5a5a, selected_outline=0xff6600,
selected_label=0x525252, style=Button.ROUNDRECT)
buttons.append(button_sound) # adding this button to the buttons group
# Add this button to view2 Group
view3.append(button_sound.group)
#pylint: disable=global-statement
def switch_view(what_view):
global view_live
if what_view == 1:
hideLayer(view2)
hideLayer(view3)
button_view1.selected = False
button_view2.selected = True
button_view3.selected = True
showLayer(view1)
view_live = 1
print("View1 On")
elif what_view == 2:
# global icon
hideLayer(view1)
hideLayer(view3)
button_view1.selected = True
button_view2.selected = False
button_view3.selected = True
showLayer(view2)
view_live = 2
print("View2 On")
else:
hideLayer(view1)
hideLayer(view2)
button_view1.selected = True
button_view2.selected = True
button_view3.selected = False
showLayer(view3)
view_live = 3
print("View3 On")
#pylint: enable=global-statement
# Set veriables and startup states
button_view1.selected = False
button_view2.selected = True
button_view3.selected = True
showLayer(view1)
hideLayer(view2)
hideLayer(view3)
view_live = 1
icon = 1
icon_name = "Ruby"
button_mode = 1
switch_state = 0
button_switch.label = "OFF"
button_switch.selected = True
# Update out Labels with display text.
text_box(feed1_label, TABS_Y,
"The text on this screen is wrapped so that all of it fits nicely into a \
text box that is ### x ###.", 30)
text_box(feed1_label, TABS_Y,
'The text on this screen is wrapped so that all of it fits nicely into a \
text box that is {} x {}.'
.format(feed1_label.bounding_box[2], feed1_label.bounding_box[3]*2), 30)
text_box(feed2_label, TABS_Y, 'Tap on the Icon button to meet a new friend.', 18)
text_box(sensors_label, TABS_Y,
"This screen can display sensor readings and tap Sound to play a WAV file.", 28)
board.DISPLAY.show(splash)
# ------------- Code Loop ------------- #
while True:
touch = ts.touch_point
light = light_sensor.value
tempC = round(adt.temperature)
tempF = tempC * 1.8 + 32
sensor_data.text = 'Touch: {}\nLight: {}\n Temp: {}°F'.format(touch, light, tempF)
# ------------- Handle Button Press Detection ------------- #
if touch: # Only do this if the screen is touched
# loop with buttons using enumerate() to number each button group as i
for i, b in enumerate(buttons):
if b.contains(touch): # Test each button to see if it was pressed
print('button%d pressed' % i)
if i == 0 and view_live != 1: # only if view1 is visable
pyportal.play_file(soundTab)
switch_view(1)
while ts.touch_point:
pass
if i == 1 and view_live != 2: # only if view2 is visable
pyportal.play_file(soundTab)
switch_view(2)
while ts.touch_point:
pass
if i == 2 and view_live != 3: # only if view3 is visable
pyportal.play_file(soundTab)
switch_view(3)
while ts.touch_point:
pass
if i == 3:
pyportal.play_file(soundBeep)
# Toggle switch button type
if switch_state == 0:
switch_state = 1
b.label = "ON"
b.selected = False
pixel.fill(WHITE)
print("Swich ON")
else:
switch_state = 0
b.label = "OFF"
b.selected = True
pixel.fill(BLACK)
print("Swich OFF")
# for debounce
while ts.touch_point:
pass
print("Swich Pressed")
if i == 4:
pyportal.play_file(soundBeep)
# Momentary button type
b.selected = True
print('Button Pressed')
button_mode = numberUP(button_mode, 5)
if button_mode == 1:
pixel.fill(RED)
elif button_mode == 2:
pixel.fill(YELLOW)
elif button_mode == 3:
pixel.fill(GREEN)
elif button_mode == 4:
pixel.fill(BLUE)
elif button_mode == 5:
pixel.fill(PURPLE)
switch_state = 1
button_switch.label = "ON"
button_switch.selected = False
# for debounce
while ts.touch_point:
pass
print("Button released")
b.selected = False
if i == 5 and view_live == 2: # only if view2 is visable
pyportal.play_file(soundBeep)
b.selected = True
while ts.touch_point:
pass
print("Icon Button Pressed")
icon = numberUP(icon, 3)
if icon == 1:
icon_name = "Ruby"
elif icon == 2:
icon_name = "Gus"
elif icon == 3:
icon_name = "Billie"
b.selected = False
text_box(feed2_label, TABS_Y,
"Every time you tap the Icon button the icon image will \
change. Say hi to {}!".format(icon_name), 18)
set_image(icon_group, "/images/"+icon_name+".bmp")
if i == 6 and view_live == 3: # only if view3 is visable
b.selected = True
while ts.touch_point:
pass
print("Sound Button Pressed")
pyportal.play_file(soundDemo)
b.selected = False
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yottatix/btse-python | btse_futures/order.py | 1c5019d0a68dff797afc70c4cc32c1950c28af4e | import json
from btse_futures.constants import OrderType, Side, TimeInForce
class Order:
"""
Class to represent a BTSE Order
...
Attributes
----------
size : int
order quantity or size. e.g. 1
price : float
price. e.g. 7000.0
side: str
order side. BUY or SELL
time_in_force: str
time the order is in force. Possible options defined in TimeInForce. e.g. GTC
symbol: str
instrument symbol. e.g. BTCPFC
type: str
order type. "LIMIT", "MARKET", or "OCO"
txType: str
transaction type
postOnly: bool
Is order post only?
reduceOnly: bool
Is order reduce only?
triggerPrice: float
Trigger price. Relevant only for LIMIT and OCO order types
stopPrice: float
Stop price.
trailValue: float
Trail value.
clOrderId: str
User defined order id
trigger: str
If an order is a stop loss or take profit order, then this parameter determines the trigger price.
Available values are: 1. markPrice = Mark Price (Default) and 2. lastPrice = Last transacted Price
Documentation: https://www.btse.com/apiexplorer/futures/?shell#tocs_orderformv2
"""
def __init__(self, size: int, price: float, side: str, time_in_force: str, symbol: str, type: str, txType: str, postOnly: bool, reduceOnly: bool, triggerPrice: float, stopPrice: float = None, trailValue: float = None, clOrderId: str = None, trigger: str = None) -> None:
assert(isinstance(size, int))
assert(isinstance(price, float))
assert(isinstance(side, str))
assert(isinstance(time_in_force, str))
assert(isinstance(symbol, str))
assert(isinstance(type, str))
assert(isinstance(postOnly, bool))
assert(isinstance(reduceOnly, bool))
assert(isinstance(triggerPrice, float))
self.size = size
self.price = price
self.side = side
self.time_in_force = time_in_force
self.symbol = symbol
self.type = type
self.txType = txType
self.postOnly = postOnly
self.reduceOnly = reduceOnly
self.triggerPrice = triggerPrice
self.stopPrice = stopPrice
self.trailValue = trailValue
self.clOrderId = clOrderId
self.trigger = trigger
@property
def quantity(self):
return self.size
def to_json(self):
json_string = json.dumps(self.order_without_none_values())
print(f'json string: {json_string}')
return json_string
def order_without_none_values(self):
order_dict = self.__dict__
for key, value in list(order_dict.items()):
if value is None:
del order_dict[key]
return order_dict
class OpenOrder:
"""
open order endpoint response format
https://www.btse.com/apiexplorer/futures/#tocs_positionrespv2_1
Example:
--------
`{
"orderType": 0,
"price": 6875,
"size": 4,
"side": "BUY",
"filledSize": 3,
"orderValue": 20.625,
"pegPriceMin": 0,
"pegPriceMax": 0,
"pegPriceDeviation": 0,
"cancelDuration": 0,
"timestamp": 1576661434072,
"orderID": "string",
"stealth": 0.2,
"triggerOrder": true,
"triggered": true,
"triggerPrice": 0,
"triggerOriginalPrice": 0,
"triggerOrderType": 1001,
"triggerTrailingStopDeviation": 0,
"triggerStopPrice": 0,
"symbol": "string",
"trailValue": 0,
"clOrderID": "market001",
"reduceOnly": true,
"orderState": "string"
}`
"""
def __init__(self) -> None:
self.orderType = 0
self.price = 0
self.size = 0
self.side = ''
self.filledSize = 0
self.orderValue = 0.0
self.pegPriceMin = 0
self.pegPriceMax = 0
self.pegPriceDeviation = 0
self.cancelDuration = 0
self.timestamp = 0
self.orderID = ''
self.stealth = 0.0
self.triggerOrder = ''
self.triggered = ''
self.triggerPrice = 0
self.triggerOriginalPrice = 0
self.triggerOrderType = 0
self.triggerTrailingStopDeviation = 0
self.triggerStopPrice = 0
self.symbol = ''
self.trailValue = 0
self.clOrderID = ''
self.reduceOnly = ''
self.orderState = ''
@staticmethod
def from_dict(data):
open_order = OpenOrder()
open_order.orderType = data.get('orderType')
open_order.price = data.get('price')
open_order.size = data.get('size')
open_order.side = data.get('side')
open_order.filledSize = data.get('filledSize')
open_order.orderValue = data.get('orderValue')
open_order.pegPriceMin = data.get('pegPriceMin')
open_order.pegPriceMax = data.get('pegPriceMax')
open_order.pegPriceDeviation = data.get('pegPriceDeviation')
open_order.cancelDuration = data.get('cancelDuration')
open_order.timestamp = data.get('timestamp')
open_order.orderID = data.get('orderID')
open_order.stealth = data.get('stealth')
open_order.triggerOrder = data.get('triggerOrder')
open_order.triggered = data.get('triggered')
open_order.triggerPrice = data.get('triggerPrice')
open_order.triggerOriginalPrice = data.get('triggerOriginalPrice')
open_order.triggerOrderType = data.get('triggerOrderType')
open_order.triggerTrailingStopDeviation = data.get(
'triggerTrailingStopDeviation')
open_order.triggerStopPrice = data.get('triggerStopPrice')
open_order.symbol = data.get('symbol')
open_order.trailValue = data.get('trailValue')
open_order.clOrderID = data.get('clOrderID')
open_order.reduceOnly = data.get('reduceOnly')
open_order.orderState = data.get('orderState')
return open_order
class OrderResponseV21:
"""
Order Response V2.1
Documentation -- https://www.btse.com/apiexplorer/futures/?shell#tocs_orderrespv2_1
"""
def __init__(self) -> None:
self.status = 0
self.symbol = ''
self.orderType = 0
self.price = 0.0
self.side = ''
self.size = 0
self.orderID = ''
self.timestamp = 0
self.triggerPrice = 0.0
self.trigger = ''
self.deviation = 0.0
self.stealth = 0.0
self.message = ''
self.avgFillPrice = 0.0
self.fillSize = 0.0
self.clOrderID = ''
@staticmethod
def from_dict(data):
order_response_v21 = OrderResponseV21()
order_response_v21.status = data.get('status')
order_response_v21.symbol = data.get('symbol')
order_response_v21.orderType = data.get('orderType')
order_response_v21.price = data.get('price')
order_response_v21.side = data.get('side')
order_response_v21.size = data.get('size')
order_response_v21.orderID = data.get('orderID')
order_response_v21.timestamp = data.get('timestamp')
order_response_v21.triggerPrice = data.get('triggerPrice')
order_response_v21.trigger = data.get('trigger')
order_response_v21.deviation = data.get('deviation')
order_response_v21.stealth = data.get('stealth')
order_response_v21.message = data.get('message')
order_response_v21.avgFillPrice = data.get('avgFillPrice')
order_response_v21.fillSize = data.get('fillSize')
order_response_v21.clOrderID = data.get('clOrderID')
return order_response_v21
| [] |
md-reddevil/blinkpy | tests/mock_responses.py | 3c7892385352079227c6251eb88257870bea0bb3 | """Simple mock responses definitions."""
from blinkpy.helpers.util import BlinkURLHandler
import blinkpy.helpers.constants as const
LOGIN_RESPONSE = {
'region': {'mock': 'Test'},
'networks': {
'1234': {'name': 'test', 'onboarded': True}
},
'authtoken': {'authtoken': 'foobar123', 'message': 'auth'}
}
class MockResponse:
"""Class for mock request response."""
def __init__(self, json_data, status_code, raw_data=None):
"""Initialize mock get response."""
self.json_data = json_data
self.status_code = status_code
self.raw_data = raw_data
def json(self):
"""Return json data from get_request."""
return self.json_data
@property
def raw(self):
"""Return raw data from get request."""
return self.raw_data
def mocked_session_send(*args, **kwargs):
"""Mock session."""
prepped = args[0]
url = prepped.url
header = prepped.headers
method = prepped.method
if method == 'GET':
expected_token = LOGIN_RESPONSE['authtoken']['authtoken']
if header['TOKEN_AUTH'] != expected_token:
response = {'message': 'Not Authorized', 'code': 400}
status = 400
elif url == 'use_bad_response':
response = {'foo': 'bar'}
status = 200
elif url == 'reauth':
response = {'message': 'REAUTH', 'code': 777}
status = 777
else:
response = {'test': 'foo'}
status = 200
elif method == 'POST':
if url in (const.LOGIN_URL, const.LOGIN_BACKUP_URL):
response = LOGIN_RESPONSE
status = 200
elif url == 'http://wrong.url/' or url is None:
response = {'message': 'Error', 'code': 404}
status = 404
else:
response = {'message': 'foo', 'code': 200}
status = 200
return MockResponse(response, status)
class MockURLHandler(BlinkURLHandler):
"""Mocks URL Handler in blinkpy module."""
pass
| [] |
steveschulze/Photometry | fits_tools.py | 3bc4ce457a270962321176d0e3e288b5a96cd34b | from astropy import coordinates as coord
from astropy import wcs
from astropy.io import fits
from astropy import units as u
from misc import bcolors
import numpy as np
import os
def convert_hms_dd(RA, DEC):
'''
Convert HMS to DD system
'''
if (':' in RA) and (':' in DEC):
Coord_dd = coord.SkyCoord(RA, DEC, unit=(u.hour,u.degree), frame='icrs')
RA_dd = Coord_dd.ra.deg
Dec_dd = Coord_dd.dec.deg
elif (not (':' in RA) and not (':' in DEC)) and (('.' in RA) and ('.' in DEC)):
RA_dd, Dec_dd = float(RA), float(DEC)
else:
print(bcolors.FAIL + 'Coordinates have wrong format.' + bcolors.ENDC)
sys.exit()
return RA_dd, Dec_dd
def get_header(FILE, KEYWORD):
'''
Get keyword from fits file
'''
header = fits.getheader(FILE)
return header[KEYWORD]
def pix2arcsec(FITS):
'''
Get pixel scale
'''
hdu = fits.open(FITS)
if len(hdu) > 1:
header = fits.getheader(FITS, 0)
header += fits.getheader(FITS, 1)
else:
header = fits.getheader(FITS)
hdu_wcs = wcs.WCS(header)
return np.median(wcs.utils.proj_plane_pixel_scales(hdu_wcs)) * 3600
def sky2xy (FITS, RA=False, DEC=False, CAT=None):
'''
Coordinate transformation: sky -> xy
'''
if CAT == None:
if RA != False and DEC != False:
cmd=('sky2xy %s %s %s | grep -v off' %(FITS, RA, DEC))
program_call = os.popen(cmd)
xy = []
for line in program_call:
xy=np.array(line.strip().split()[-2:]).astype(float)
if len(xy) > 0:
return xy
else:
cmd =("more %s | awk '{print $1,$2}' > %s" %(CAT, CAT.replace(CAT.split('.')[-1], 'reg')))
os.system(cmd)
cmd = ("sky2xy %s @%s | grep -v off | awk '{print $5, $6}'" %(FITS, CAT.replace(CAT.split('.')[-1], 'reg')))
cat = os.popen(cmd)
xy = []
for line in cat:
xy.append(list(map(float, line.replace('\n', '').split())))
return np.array(xy)
def xy2sky (FITSFILE,X,Y):
'''
Coordinate transformation: xy -> sky
'''
program_call = os.popen('xy2sky %s %s %s' %(FITSFILE, X, Y))
sky = []
for line in program_call:
sky.append(line.strip().split()[:2])
return sky
| [((35, 10, 35, 30), 'astropy.io.fits.getheader', 'fits.getheader', ({(35, 25, 35, 29): 'FILE'}, {}), '(FILE)', False, 'from astropy.io import fits\n'), ((44, 8, 44, 23), 'astropy.io.fits.open', 'fits.open', ({(44, 18, 44, 22): 'FITS'}, {}), '(FITS)', False, 'from astropy.io import fits\n'), ((51, 13, 51, 28), 'astropy.wcs.WCS', 'wcs.WCS', ({(51, 21, 51, 27): 'header'}, {}), '(header)', False, 'from astropy import wcs\n'), ((90, 16, 90, 61), 'os.popen', 'os.popen', ({(90, 25, 90, 60): "'xy2sky %s %s %s' % (FITSFILE, X, Y)"}, {}), "('xy2sky %s %s %s' % (FITSFILE, X, Y))", False, 'import os\n'), ((16, 13, 16, 74), 'astropy.coordinates.SkyCoord', 'coord.SkyCoord', (), '', True, 'from astropy import coordinates as coord\n'), ((46, 11, 46, 34), 'astropy.io.fits.getheader', 'fits.getheader', ({(46, 26, 46, 30): 'FITS', (46, 32, 46, 33): '0'}, {}), '(FITS, 0)', False, 'from astropy.io import fits\n'), ((47, 12, 47, 35), 'astropy.io.fits.getheader', 'fits.getheader', ({(47, 27, 47, 31): 'FITS', (47, 33, 47, 34): '(1)'}, {}), '(FITS, 1)', False, 'from astropy.io import fits\n'), ((49, 11, 49, 31), 'astropy.io.fits.getheader', 'fits.getheader', ({(49, 26, 49, 30): 'FITS'}, {}), '(FITS)', False, 'from astropy.io import fits\n'), ((73, 2, 73, 16), 'os.system', 'os.system', ({(73, 12, 73, 15): 'cmd'}, {}), '(cmd)', False, 'import os\n'), ((75, 9, 75, 22), 'os.popen', 'os.popen', ({(75, 18, 75, 21): 'cmd'}, {}), '(cmd)', False, 'import os\n'), ((82, 9, 82, 21), 'numpy.array', 'np.array', ({(82, 18, 82, 20): 'xy'}, {}), '(xy)', True, 'import numpy as np\n'), ((52, 18, 52, 60), 'astropy.wcs.utils.proj_plane_pixel_scales', 'wcs.utils.proj_plane_pixel_scales', ({(52, 52, 52, 59): 'hdu_wcs'}, {}), '(hdu_wcs)', False, 'from astropy import wcs\n'), ((64, 18, 64, 31), 'os.popen', 'os.popen', ({(64, 27, 64, 30): 'cmd'}, {}), '(cmd)', False, 'import os\n')] |
neurom-iot/n3ml | test_stbp_snn_eval.py | 39c6b50661f293d58b4b37ef613643860724bb24 | import argparse
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from n3ml.model import DynamicModel_STBP_SNN
def validate(val_loader, model, encoder, criterion, opt):
model.eval()
total_images = 0
num_corrects = 0
total_loss = 0
with torch.no_grad():
for step, (images, labels) in enumerate(val_loader):
images = images.cuda()
labels = labels.cuda()
preds = model(encoder, images, opt.num_steps)
labels_ = torch.zeros(torch.numel(labels), 10, device=labels.device)
labels_ = labels_.scatter_(1, labels.view(-1, 1), 1)
loss = criterion(preds, labels_)
num_corrects += torch.argmax(preds, dim=1).eq(labels).sum(dim=0)
total_loss += loss.cpu().detach().numpy() * images.size(0)
total_images += images.size(0)
val_acc = num_corrects.float() / total_images
val_loss = total_loss / total_images
return val_acc, val_loss
def app(opt):
print(opt)
val_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST(
opt.data,
train=False,
download=True,
transform=torchvision.transforms.Compose([transforms.ToTensor()])),
batch_size=opt.batch_size)
state_dict = torch.load(opt.pretrained)
model = DynamicModel_STBP_SNN(batch_size=opt.batch_size)
for m in state_dict['arch']:
model.add_module(m[0], m[1])
if torch.cuda.is_available():
model.cuda()
encoder = lambda x: (x > torch.rand(x.size(), device=x.device)).float()
criterion = nn.MSELoss()
acc, loss = validate(val_loader, model, encoder, criterion, opt)
print("In test, loss: {} - acc: {}".format(loss, acc))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data', default='data')
parser.add_argument('--batch_size', default=100, type=int)
parser.add_argument('--num_steps', default=15, type=int)
parser.add_argument('--pretrained', default='pretrained/stbp_dynamic_acc_9897.pt')
app(parser.parse_args())
| [((50, 17, 50, 43), 'torch.load', 'torch.load', ({(50, 28, 50, 42): 'opt.pretrained'}, {}), '(opt.pretrained)', False, 'import torch\n'), ((52, 12, 52, 60), 'n3ml.model.DynamicModel_STBP_SNN', 'DynamicModel_STBP_SNN', (), '', False, 'from n3ml.model import DynamicModel_STBP_SNN\n'), ((56, 7, 56, 32), 'torch.cuda.is_available', 'torch.cuda.is_available', ({}, {}), '()', False, 'import torch\n'), ((61, 16, 61, 28), 'torch.nn.MSELoss', 'nn.MSELoss', ({}, {}), '()', True, 'import torch.nn as nn\n'), ((68, 13, 68, 38), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ({}, {}), '()', False, 'import argparse\n'), ((18, 9, 18, 24), 'torch.no_grad', 'torch.no_grad', ({}, {}), '()', False, 'import torch\n'), ((24, 34, 24, 53), 'torch.numel', 'torch.numel', ({(24, 46, 24, 52): 'labels'}, {}), '(labels)', False, 'import torch\n'), ((47, 54, 47, 75), 'torchvision.transforms.ToTensor', 'transforms.ToTensor', ({}, {}), '()', True, 'import torchvision.transforms as transforms\n'), ((29, 28, 29, 54), 'torch.argmax', 'torch.argmax', (), '', False, 'import torch\n')] |
govex/python-lessons | section_07_(files)/read_csv.py | e692f48b6db008a45df0b941dee1e580f5a6c800 | # If you're new to file handling, be sure to check out with_open.py first!
# You'll also want to check out read_text.py before this example. This one is a bit more advanced.
with open('read_csv.csv', 'r') as states_file:
# Instead of leaving the file contents as a string, we're splitting the file into a list at every new line, and we save that list into the variable states
states = states_file.read().split("\n")
# Since this is a spreadsheet in comma separated values (CSV) format, we can think of states as a list of rows.
# But we'll need to split the columns into a list as well!
for index, state in enumerate(states):
states[index] = state.split(",")
# Now we have a nested list with all of the information!
# Our file looks like this:
# State, Population Estimate, Percent of Total population
# California, 38332521, 11.91%
# Texas, 26448193, 8.04%
# ...
# Our header row is at state[0], so we can use that to display the information in a prettier way.
for state in states[1:]: # We use [1:] so we skip the header row.
# state[0] is the first column in the row, which contains the name of the state.
print("\n---{0}---".format(state[0]))
for index, info in enumerate(state[1:]): # We use [1:] so we don't repeat the state name.
print("{0}:\t{1}".format(states[0][index+1], info))
# states is the full list of all of the states. It's a nested list. The outer list contains the rows, each inner list contains the columns in that row.
# states[0] refers to the header row of the list
# So states[0][0] would refer to "State", states[0][1] would refer to "Population Estimate", and states[0][2] would refer to "Percent of total population"
# state is one state within states. state is also a list, containing the name, population, and percentage of that particular state.
# So the first time through the loop, state[0] would refer to "California", state[1] would refer to 38332521, and state[2] would refer to 11.91%
# Since state is being create by the for loop in line 24, it gets a new value each time through.
# We're using enumerate to get the index (slicing number) of the column we're on, along with the information.
# That way we can pair the column name with the information, as shown in line 30.
# NOTE: Since we're slicing from [1:] in line 29, we need to increase the index by + 1, otherwise our headers will be off by one.
# Sample output:
# ---"California"---
# "Population Estimate": 38332521
# "Percent of Total population": "11.91%"
# ---"Texas"---
# "Population Estimate": 26448193
# "Percent of Total population": "8.04%"
# ---"New York"---
# "Population Estimate": 19651127
# "Percent of Total population": "6.19%"
| [] |
tinve/kaggle_melanoma | kaggle_melanoma/schedulers.py | 6d2d16d62a394fd9cc2498bdf1a19ce60fe047eb | import math
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.optimizer import Optimizer
class PolyLR(_LRScheduler):
"""
Sets the learning rate of each parameter group according to poly learning rate policy
"""
def __init__(self, optimizer, max_iter=90000, power=0.9, last_epoch=-1):
self.max_iter = max_iter
self.power = power
super().__init__(optimizer, last_epoch)
def get_lr(self):
return [base_lr * (1 - float(self.last_epoch) / self.max_iter) ** self.power for base_lr in self.base_lrs]
func_zoo = {
"cosine_decay": lambda epoch, step, len_epoch, total_epoch: 0.5
* (math.cos(step * math.pi / (total_epoch * len_epoch)) + 1)
}
class CosineWarmRestart:
def __init__(
self,
optimizer: Optimizer,
func: str = "cosine_decay",
warmup: bool = True,
warmup_epoch: int = 1,
period: int = 10,
min_lr: float = 1e-5,
low_epoch: int = 1,
):
# self.base_lrs = list(map(lambda group: group["lr"], optimizer.param_groups))[0]
self.base_lrs = [x["lr"] for x in optimizer.param_groups][0]
self.optimizer = optimizer
self.warmup = warmup
self.warmup_epoch = warmup_epoch
self.period = period
self.cos_period = period - low_epoch
self.low_epoch = low_epoch
self.lr_func = func_zoo[func]
self.min_lr = min_lr
def cosine_step(self, current_epoch: int, global_step: int, len_epoch: int) -> float:
if self.warmup and current_epoch < self.warmup_epoch:
lr = self.base_lrs * float(1 + global_step) / (self.warmup_epoch * len_epoch)
else:
lr = self.base_lrs * self.lr_func(current_epoch, global_step, len_epoch, self.cos_period)
lr = max(self.min_lr, lr)
for param_group in self.optimizer.param_groups:
param_group["lr"] = lr
return lr
def step(self, current_epoch: int, global_step: int, len_epoch: int) -> float:
current_epoch = current_epoch % self.period
if current_epoch >= self.period - self.low_epoch:
global_step = len_epoch * self.cos_period
else:
global_step = global_step % (self.period * len_epoch)
return self.cosine_step(current_epoch, global_step, len_epoch)
| [((23, 7, 23, 59), 'math.cos', 'math.cos', ({(23, 16, 23, 58): '(step * math.pi / (total_epoch * len_epoch))'}, {}), '(step * math.pi / (total_epoch * len_epoch))', False, 'import math\n')] |
PumpkinYing/GAT | data/data/__init__.py | 723a20fcd9f915123d46ef4ef03eeadb6910635a | from .dataset import load_data | [] |
federicosapienza/InboxNotionTelegramBot | utils.py | 031d5e78cd352dfb692b93f3e0b421695f1dc18e | import json
import logging
logger = logging.getLogger(__name__)
with open('configuration.json') as f:
config = json.load(f)
TELEGRAM_TOKEN = config["telegram-bot-token"]
NOTION_TOKEN = config["notion-token"]
NOTION_TABLE_URL = config["inbox_table"]["table_url"]
def check_allowed_user(user_id):
"""
check if allowed user
:param user_id: telegram user id
:return True if user is valid , False otherwise
"""
valid_user = config["allowed_user_id"]
user_id = str(user_id)
return user_id == valid_user
def restrict_action(handled_action):
"""
Wrapper for creating a private bot
:param handled_action: the action to perform
"""
def check_private(update, context):
if not (check_allowed_user(update.message.from_user.id)):
logging.warning("An unauthorized user attempted to use the bot. username: {}, id: {} .".format(
update.message.from_user.username, update.message.from_user.id
))
return
else:
return handled_action(update, context)
return check_private
| [((4, 9, 4, 36), 'logging.getLogger', 'logging.getLogger', ({(4, 27, 4, 35): '__name__'}, {}), '(__name__)', False, 'import logging\n'), ((7, 13, 7, 25), 'json.load', 'json.load', ({(7, 23, 7, 24): 'f'}, {}), '(f)', False, 'import json\n')] |
timgates42/enaml | enaml/core/byteplay/__init__.py | 054efe6a4047d84f2fff718d656a64a2363884dc | #------------------------------------------------------------------------------
# Copyright (c) 2013-2018, Nucleic Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#------------------------------------------------------------------------------
from ...compat import USE_WORDCODE
if USE_WORDCODE:
from .wbyteplay import *
else:
from .byteplay3 import *
| [] |
artemigkh/cassiopeia | cassiopeia/datastores/riotapi/match.py | fa78cb8f86ea21857916a707d04de6a05498033e | from time import time
from typing import Type, TypeVar, MutableMapping, Any, Iterable, Generator, Union
import arrow
import datetime
import math
from datapipelines import DataSource, PipelineContext, Query, NotFoundError, validate_query
from .common import RiotAPIService, APINotFoundError
from ...data import Platform, Season, Queue, SEASON_IDS, QUEUE_IDS
from ...dto.match import MatchDto, MatchListDto, TimelineDto
from ..uniquekeys import convert_region_to_platform
T = TypeVar("T")
def _get_current_time(query: MutableMapping[str, Any], context: PipelineContext = None) -> int:
return int(time()) * 1000
class MatchAPI(RiotAPIService):
@DataSource.dispatch
def get(self, type: Type[T], query: MutableMapping[str, Any], context: PipelineContext = None) -> T:
pass
@DataSource.dispatch
def get_many(self, type: Type[T], query: MutableMapping[str, Any], context: PipelineContext = None) -> Iterable[T]:
pass
_validate_get_match_query = Query. \
has("id").as_(int).also. \
has("platform").as_(Platform)
@get.register(MatchDto)
@validate_query(_validate_get_match_query, convert_region_to_platform)
def get_match(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> MatchDto:
url = "https://{platform}.api.riotgames.com/lol/match/v4/matches/{id}".format(platform=query["platform"].value.lower(), id=query["id"])
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matches/id")
data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError as error:
raise NotFoundError(str(error)) from error
data["gameId"] = query["id"]
data["region"] = query["platform"].region.value
for p in data["participantIdentities"]:
aid = p.get("player", {}).get("currentAccountId", None)
if aid == 0:
p["player"]["bot"] = True
return MatchDto(data)
_validate_get_many_match_query = Query. \
has("ids").as_(Iterable).also. \
has("platform").as_(Platform)
@get_many.register(MatchDto)
@validate_query(_validate_get_many_match_query, convert_region_to_platform)
def get_many_match(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[MatchDto, None, None]:
def generator():
for id in query["ids"]:
url = "https://{platform}.api.riotgames.com/lol/match/v4/matches/{id}".format(platform=query["platform"].value.lower(), id=id)
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matches/id")
data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError as error:
raise NotFoundError(str(error)) from error
for participant in data["participants"]:
participant.setdefault("runes", [])
for p in data["participantIdentities"]:
aid = p.get("player", {}).get("currentAccountId", None)
if aid == 0:
p["player"]["bot"] = True
data["gameId"] = id
data["region"] = query["platform"].region.value
yield MatchDto(data)
return generator()
_validate_get_match_list_query = Query. \
has("accountId").as_(str).also. \
has("platform").as_(Platform).also. \
has("beginTime").as_(int).also. \
can_have("endTime").as_(int).also. \
has("beginIndex").as_(int).also. \
has("maxNumberOfMatches").as_(float).also. \
can_have("seasons").as_(Iterable).also. \
can_have("champion.ids").as_(Iterable).also. \
can_have("queues").as_(Iterable)
@get.register(MatchListDto)
@validate_query(_validate_get_match_list_query, convert_region_to_platform)
def get_match_list(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> MatchListDto:
params = {}
riot_index_interval = 100
riot_date_interval = datetime.timedelta(days=7)
begin_time = query["beginTime"] # type: arrow.Arrow
end_time = query.get("endTime", arrow.now()) # type: arrow.Arrow
if isinstance(begin_time, int):
begin_time = arrow.get(begin_time / 1000)
if isinstance(end_time, int):
end_time = arrow.get(end_time / 1000)
def determine_calling_method(begin_time, end_time) -> str:
"""Returns either "by_date" or "by_index"."""
matches_per_date_interval = 10 # This is an assumption
seconds_per_day = (60 * 60 * 24)
riot_date_interval_in_days = riot_date_interval.total_seconds() / seconds_per_day # in units of days
npulls_by_date = (end_time - begin_time).total_seconds() / seconds_per_day / riot_date_interval_in_days
npulls_by_index = (arrow.now() - begin_time).total_seconds() / seconds_per_day / riot_date_interval_in_days * matches_per_date_interval / riot_index_interval
if math.ceil(npulls_by_date) < math.ceil(npulls_by_index):
by = "by_date"
else:
by = "by_index"
return by
calling_method = determine_calling_method(begin_time, end_time)
if calling_method == "by_date":
params["beginTime"] = begin_time.timestamp * 1000
if "endTime" in query:
params["endTime"] = min((begin_time + riot_date_interval).timestamp * 1000, query["endTime"])
else:
params["endTime"] = (begin_time + riot_date_interval).timestamp * 1000
else:
params["beginIndex"] = query["beginIndex"]
params["endIndex"] = query["beginIndex"] + min(riot_index_interval, query["maxNumberOfMatches"])
params["endIndex"] = int(params["endIndex"])
if "seasons" in query:
seasons = {Season(season) for season in query["seasons"]}
params["season"] = {SEASON_IDS[season] for season in seasons}
else:
seasons = set()
if "champion.ids" in query:
champions = query["champion.ids"]
params["champion"] = champions
else:
champions = set()
if "queues" in query:
queues = {Queue(queue) for queue in query["queues"]}
params["queue"] = {QUEUE_IDS[queue] for queue in queues}
else:
queues = set()
url = "https://{platform}.api.riotgames.com/lol/match/v4/matchlists/by-account/{accountId}".format(platform=query["platform"].value.lower(), accountId=query["accountId"])
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matchlists/by-account/accountId")
data = self._get(url, params, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError:
data = {"matches": []}
data["accountId"] = query["accountId"]
data["region"] = query["platform"].region.value
data["season"] = seasons
data["champion"] = champions
data["queue"] = queues
if calling_method == "by_index":
data["beginIndex"] = params["beginIndex"]
data["endIndex"] = params["endIndex"]
data["maxNumberOfMatches"] = query["maxNumberOfMatches"]
else:
data["beginTime"] = params["beginTime"]
data["endTime"] = params["endTime"]
for match in data["matches"]:
match["accountId"] = query["accountId"]
match["region"] = Platform(match["platformId"]).region.value
return MatchListDto(data)
_validate_get_many_match_list_query = Query. \
has("accountIds").as_(Iterable).also. \
has("platform").as_(Platform).also. \
can_have("beginTime").as_(int).also. \
can_have("endTime").as_(int).also. \
can_have("beginIndex").as_(int).also. \
can_have("endIndex").as_(int).also. \
can_have("seasons").as_(Iterable).also. \
can_have("champion.ids").as_(Iterable).also. \
can_have("queues").as_(Iterable)
@get_many.register(MatchListDto)
@validate_query(_validate_get_many_match_list_query, convert_region_to_platform)
def get_many_match_list(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[MatchListDto, None, None]:
params = {}
if "beginIndex" in query:
params["beginIndex"] = query["beginIndex"]
if "endIndex" in query:
params["endIndex"] = query["endIndex"]
if "seasons" in query:
seasons = {Season(season) for season in query["seasons"]}
params["season"] = {SEASON_IDS[season] for season in seasons}
else:
seasons = set()
if "champion.ids" in query:
params["champion"] = {query["champion.ids"]}
if "queues" in query:
queues = {Queue(queue) for queue in query["queues"]}
params["queue"] = {QUEUE_IDS[queue] for queue in queues}
else:
queues = set()
def generator():
for id in query["accountIds"]:
url = "https://{platform}.api.riotgames.com/lol/match/v4/matchlists/by-account/{accountId}".format(platform=query["platform"].value.lower(), accountId=id)
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matchlists/by-account/accountId")
data = self._get(url, params, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError as error:
raise NotFoundError(str(error)) from error
data["accountId"] = id
data["region"] = query["platform"].region.value
if "beginIndex" in query:
data["beginIndex"] = query["beginIndex"]
if "endIndex" in query:
data["endIndex"] = query["endIndex"]
if "seasons" in query:
data["seasons"] = seasons
if "champion.ids" in query:
data["champion"] = params["champion"]
if "queues" in query:
params["queue"] = queues
yield MatchListDto(data)
return generator()
_validate_get_timeline_query = Query. \
has("id").as_(int).also. \
has("platform").as_(Platform)
@get.register(TimelineDto)
@validate_query(_validate_get_timeline_query, convert_region_to_platform)
def get_match_timeline(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> TimelineDto:
url = "https://{platform}.api.riotgames.com/lol/match/v4/timelines/by-match/{id}".format(platform=query["platform"].value.lower(), id=query["id"])
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "timelines/by-match/id")
data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError as error:
raise NotFoundError(str(error)) from error
data["matchId"] = query["id"]
data["region"] = query["platform"].region.value
return TimelineDto(data)
_validate_get_many_timeline_query = Query. \
has("ids").as_(Iterable).also. \
has("platform").as_(Platform)
@get_many.register(TimelineDto)
@validate_query(_validate_get_many_timeline_query, convert_region_to_platform)
def get_many_match_timeline(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[TimelineDto, None, None]:
def generator():
for id in query["ids"]:
url = "https://{platform}.api.riotgames.com/lol/match/v4/timelines/by-match/{id}".format(platform=query["platform"].value.lower(), id=id)
try:
app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "timelines/by-match/id")
data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter)
except APINotFoundError as error:
raise NotFoundError(str(error)) from error
data["matchId"] = id
data["region"] = query["platform"].region.value
yield TimelineDto(data)
return generator()
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fochoao/cpython | Lib/site-packages/hackedit/vendor/jedi/cache.py | 3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9 | """
This caching is very important for speed and memory optimizations. There's
nothing really spectacular, just some decorators. The following cache types are
available:
- module caching (`load_parser` and `save_parser`), which uses pickle and is
really important to assure low load times of modules like ``numpy``.
- ``time_cache`` can be used to cache something for just a limited time span,
which can be useful if there's user interaction and the user cannot react
faster than a certain time.
This module is one of the reasons why |jedi| is not thread-safe. As you can see
there are global variables, which are holding the cache information. Some of
these variables are being cleaned after every API usage.
"""
import time
import os
import sys
import json
import hashlib
import gc
import inspect
import shutil
import re
try:
import cPickle as pickle
except ImportError:
import pickle
from jedi import settings
from jedi import common
from jedi import debug
_time_caches = {}
# for fast_parser, should not be deleted
parser_cache = {}
class ParserCacheItem(object):
def __init__(self, parser, change_time=None):
self.parser = parser
if change_time is None:
change_time = time.time()
self.change_time = change_time
def clear_time_caches(delete_all=False):
""" Jedi caches many things, that should be completed after each completion
finishes.
:param delete_all: Deletes also the cache that is normally not deleted,
like parser cache, which is important for faster parsing.
"""
global _time_caches
if delete_all:
for cache in _time_caches.values():
cache.clear()
parser_cache.clear()
else:
# normally just kill the expired entries, not all
for tc in _time_caches.values():
# check time_cache for expired entries
for key, (t, value) in list(tc.items()):
if t < time.time():
# delete expired entries
del tc[key]
def time_cache(time_add_setting):
"""
s
This decorator works as follows: Call it with a setting and after that
use the function with a callable that returns the key.
But: This function is only called if the key is not available. After a
certain amount of time (`time_add_setting`) the cache is invalid.
"""
def _temp(key_func):
dct = {}
_time_caches[time_add_setting] = dct
def wrapper(*args, **kwargs):
generator = key_func(*args, **kwargs)
key = next(generator)
try:
expiry, value = dct[key]
if expiry > time.time():
return value
except KeyError:
pass
value = next(generator)
time_add = getattr(settings, time_add_setting)
if key is not None:
dct[key] = time.time() + time_add, value
return value
return wrapper
return _temp
@time_cache("call_signatures_validity")
def cache_call_signatures(evaluator, call, source, user_pos):
"""This function calculates the cache key."""
index = user_pos[0] - 1
lines = common.splitlines(source)
before_cursor = lines[index][:user_pos[1]]
other_lines = lines[call.start_pos[0]:index]
whole = '\n'.join(other_lines + [before_cursor])
before_bracket = re.match(r'.*\(', whole, re.DOTALL)
module_path = call.get_parent_until().path
yield None if module_path is None else (module_path, before_bracket, call.start_pos)
yield evaluator.eval_element(call)
def underscore_memoization(func):
"""
Decorator for methods::
class A(object):
def x(self):
if self._x:
self._x = 10
return self._x
Becomes::
class A(object):
@underscore_memoization
def x(self):
return 10
A now has an attribute ``_x`` written by this decorator.
"""
name = '_' + func.__name__
def wrapper(self):
try:
return getattr(self, name)
except AttributeError:
result = func(self)
if inspect.isgenerator(result):
result = list(result)
setattr(self, name, result)
return result
return wrapper
def memoize_method(method):
"""A normal memoize function."""
def wrapper(self, *args, **kwargs):
dct = self.__dict__.setdefault('_memoize_method_dct', {})
key = (args, frozenset(kwargs.items()))
try:
return dct[key]
except KeyError:
result = method(self, *args, **kwargs)
dct[key] = result
return result
return wrapper
def memoize_function(obj):
""" A normal memoize function for memoizing free functions. """
cache = obj.cache = {}
def memoizer(*args, **kwargs):
key = str(args) + str(kwargs)
if key not in cache:
cache[key] = obj(*args, **kwargs)
return cache[key]
return memoizer
def cache_star_import(func):
@time_cache("star_import_cache_validity")
def wrapper(self):
yield self.base # The cache key
yield func(self)
return wrapper
def _invalidate_star_import_cache_module(module, only_main=False):
""" Important if some new modules are being reparsed """
try:
t, modules = _time_caches['star_import_cache_validity'][module]
except KeyError:
pass
else:
del _time_caches['star_import_cache_validity'][module]
def invalidate_star_import_cache(path):
"""On success returns True."""
try:
parser_cache_item = parser_cache[path]
except KeyError:
pass
else:
_invalidate_star_import_cache_module(parser_cache_item.parser.module)
def load_parser(path):
"""
Returns the module or None, if it fails.
"""
p_time = os.path.getmtime(path) if path else None
try:
parser_cache_item = parser_cache[path]
if not path or p_time <= parser_cache_item.change_time:
return parser_cache_item.parser
else:
# In case there is already a module cached and this module
# has to be reparsed, we also need to invalidate the import
# caches.
_invalidate_star_import_cache_module(parser_cache_item.parser.module)
except KeyError:
if settings.use_filesystem_cache:
return ParserPickling.load_parser(path, p_time)
def save_parser(path, parser, pickling=True):
try:
p_time = None if path is None else os.path.getmtime(path)
except OSError:
p_time = None
pickling = False
item = ParserCacheItem(parser, p_time)
parser_cache[path] = item
if settings.use_filesystem_cache and pickling:
ParserPickling.save_parser(path, item)
class ParserPickling(object):
version = 24
"""
Version number (integer) for file system cache.
Increment this number when there are any incompatible changes in
parser representation classes. For example, the following changes
are regarded as incompatible.
- Class name is changed.
- Class is moved to another module.
- Defined slot of the class is changed.
"""
def __init__(self):
self.__index = None
self.py_tag = 'cpython-%s%s' % sys.version_info[:2]
"""
Short name for distinguish Python implementations and versions.
It's like `sys.implementation.cache_tag` but for Python < 3.3
we generate something similar. See:
http://docs.python.org/3/library/sys.html#sys.implementation
.. todo:: Detect interpreter (e.g., PyPy).
"""
def load_parser(self, path, original_changed_time):
try:
pickle_changed_time = self._index[path]
except KeyError:
return None
if original_changed_time is not None \
and pickle_changed_time < original_changed_time:
# the pickle file is outdated
return None
with open(self._get_hashed_path(path), 'rb') as f:
try:
gc.disable()
parser_cache_item = pickle.load(f)
finally:
gc.enable()
debug.dbg('pickle loaded: %s', path)
parser_cache[path] = parser_cache_item
return parser_cache_item.parser
def save_parser(self, path, parser_cache_item):
self.__index = None
try:
files = self._index
except KeyError:
files = {}
self._index = files
with open(self._get_hashed_path(path), 'wb') as f:
pickle.dump(parser_cache_item, f, pickle.HIGHEST_PROTOCOL)
files[path] = parser_cache_item.change_time
self._flush_index()
@property
def _index(self):
if self.__index is None:
try:
with open(self._get_path('index.json')) as f:
data = json.load(f)
except (IOError, ValueError):
self.__index = {}
else:
# 0 means version is not defined (= always delete cache):
if data.get('version', 0) != self.version:
self.clear_cache()
self.__index = {}
else:
self.__index = data['index']
return self.__index
def _remove_old_modules(self):
# TODO use
change = False
if change:
self._flush_index(self)
self._index # reload index
def _flush_index(self):
data = {'version': self.version, 'index': self._index}
with open(self._get_path('index.json'), 'w') as f:
json.dump(data, f)
self.__index = None
def clear_cache(self):
shutil.rmtree(self._cache_directory())
def _get_hashed_path(self, path):
return self._get_path('%s.pkl' % hashlib.md5(path.encode("utf-8")).hexdigest())
def _get_path(self, file):
dir = self._cache_directory()
if not os.path.exists(dir):
os.makedirs(dir)
return os.path.join(dir, file)
def _cache_directory(self):
return os.path.join(settings.cache_directory, self.py_tag)
# is a singleton
ParserPickling = ParserPickling()
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adarshrs/Drone-Simulator-for-ROS-Kinetic | sandia_hand/ros/sandia_hand_teleop/simple_grasp/simple_grasp.py | a44eef1bcaacc55539325bba663f0c8abfd7c75b | #!/usr/bin/env python
#
# Software License Agreement (Apache License)
#
# Copyright 2013 Open Source Robotics Foundation
# Author: Morgan Quigley
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import roslib; roslib.load_manifest('sandia_hand_teleop')
import rospy
import sys
from sandia_hand_msgs.srv import SimpleGraspSrv, SimpleGraspSrvResponse, SimpleGraspWithSlew, SimpleGraspWithSlewResponse
from sandia_hand_msgs.msg import SimpleGrasp
from osrf_msgs.msg import JointCommands
g_jc_pub = None
g_jc = JointCommands()
g_prev_jc_target = JointCommands()
def grasp_srv(req):
grasp_cb(req.grasp)
return SimpleGraspSrvResponse()
def grasp_slew_srv(req):
#print "going to %s in %.3f" % (req.grasp.name, req.slew_duration)
rate = rospy.Rate(100.0)
t_start = rospy.Time.now()
t_end = t_start + rospy.Duration(req.slew_duration)
while rospy.Time.now() < t_end:
dt = (rospy.Time.now() - t_start).to_sec()
dt_norm = dt / req.slew_duration
#print "%.3f" % dt_norm
grasp_spline(req.grasp.name, req.grasp.closed_amount, dt_norm)
rate.sleep()
grasp_spline(req.grasp.name, req.grasp.closed_amount, 1.0)
return SimpleGraspWithSlewResponse()
def grasp_spline(grasp_name, closed_amount, spline_amount):
global g_jc_pub, g_jc, g_prev_jc_target
#print "request: grasp [%s] amount [%f]" % (grasp_name, closed_amount)
# save some typing
gn = grasp_name
x = closed_amount
if x < 0:
x = 0
elif x > 1:
x = 1
origin = [0] * 12
g0 = [0] * 12
if (gn == "cylindrical"):
g0 = [0,1.5,1.7, 0,1.5,1.7, 0,1.5,1.7, 0.2,.8,1.2]
elif (gn == "spherical"):
origin = [-0.7,0,0, 0.1,0,0, 0.7,0,0, 0,0,0]
g0 = [0,1.4,1.4, 0,1.4,1.4, 0,1.4,1.4, 0,0.7,0.7]
elif (gn == "prismatic"):
origin = [0,1.4,0, 0,1.4,0, 0,1.4,0, -0.1,0.8,-0.8]
g0 = [0,0,1.4, 0,0,1.4, 0,0,1.4, 0,0,1.4]
elif (gn == "finger_0_test"):
g0 = [0,1.5,1.7, 0,0,0, 0,0,0, 0,0,0]
elif (gn == "number_one"):
origin = [0,0,0, 0,1.5,1.5, 0,1.5,1.5, 0.4,0.8,1 ]
elif (gn == "peace"):
origin = [-0.2,0,0, 0.05,0,0, 0,1.5,1.5, 0.4,0.8,1 ]
elif (gn == "asl_a"):
origin = [0,1.5,1.5, 0,1.5,1.5, 0,1.5,1.5, 1.5,0.9,0.2 ]
elif (gn == "asl_b"):
origin = [0.1,0,0, 0,0,0, -0.1,0,0, 1,0.8,0.9 ]
elif (gn == "asl_c"):
origin = [0,0.7,0.9, 0,0.7,0.9, 0,0.7,0.9, 0,0.4,0.4 ]
elif (gn == "asl_d"):
origin = [0,0,0, 0,1.5,1.5, 0,1.5,1.5, 0.4,0.8,1 ]
elif (gn == "asl_e"):
origin = [0,1,1.8, 0,1,1.8, 0,1,1.8, 1.5,0.6,1]
elif (gn == "asl_f"):
origin = [0,1.3,1.2, 0.1,0,0, 0.2,0,0, 0.3,0.7,0.7 ]
elif (gn == "asl_g"):
origin = [0,1.5,0, 0,1.5,1.5, 0,1.5,1.5, 0,1,-.4 ]
elif (gn == "asl_h"):
origin = [0.1,1.5,0, 0,1.5,0, 0,1.5,1.5, 0,1,0.6 ]
elif (gn == "asl_i"):
origin = [0,1.5,1.5, 0,1.5,1.5, 0,0,0, 1.5,1.0,0.3 ]
elif (gn == "asl_j"):
origin = [0,1.5,1.5, 0,1.5,1.5, 0,0,0, 1.5,1.0,0.3 ]
g0 = [0,0,0, 0,0,0, 0,0,0, 0.5,1,1]
g1 = [0,0,0, 0,0,0, 0,0,0, 0,1,1]
elif (gn == "asl_k"):
origin = [0,0,0, 0,1.5,0, 0,1.5,1.5, 1.5,1.0,0.3]
elif (gn == "asl_l"):
origin = [0,0,0, 0,1.5,1.5, 0,1.5,1.5, 1.5,0,0]
elif (gn == "asl_m"):
origin = [0,1,1.5, 0,1,1.5, 0,1,1.5, 0,1,1]
elif (gn == "asl_n"):
origin = [0,1,1.5, 0,1,1.5, 0,1.5,1.5, 0,1,1]
elif (gn == "asl_o"):
origin = [0.1,1.3,1.2, 0,1.3,1.2, -0.1,1.3,1.2, 0.2,0.8,0.5]
elif (gn == "asl_p"):
origin = [0,0,0, 0,1.5,0, 0,1.5,1.5, 1.5,1,0.3]
elif (gn == "asl_q"):
origin = [0,1.3,1.2, 0,1.5,1.5, 0,1.5,1.5, 0.4,0.8,0.5]
elif (gn == "asl_r"):
origin = [0.1,0,0, -0.1,0,0, 0,1.5,1.5, 0,1,1]
elif (gn == "asl_s"):
origin = [0,1.5,1.5, 0,1.5,1.5, 0,1.5,1.5, 0,1,0.2]
elif (gn == "asl_t"):
origin = [-.4,1.3,1.5, 0,1.5,1.5, 0,1.5,1.5, 0.4,1,1]
elif (gn == "asl_u"):
origin = [0,0,0, 0,0,0, 0,1.5,1.5, 0,1,1]
elif (gn == "asl_v"):
origin = [-0.3,0,0, 0.1,0,0, 0,1.5,1.5, 0,1,1]
elif (gn == "asl_w"):
origin = [-0.3,0,0, 0,0,0, 0.3,0,0, 0,1,1]
elif (gn == "asl_x"):
origin = [0,0,1.5, 0,1.5,1.5, 0,1.5,1.5, 0,1,1]
elif (gn == "asl_y"):
origin = [0,1.5,1.5, 0,1.5,1.5, 0.3,0,0, 1.5,0,0]
elif (gn == "asl_z"):
origin = [0,1.0,0, 0,1.5,1.5, 0,1.5,1.5, 0.4,0.8,0.8]
g0 = [0.3,0.3,0, 0,0,0, 0,0,0, 0,0,0]
g1 = [-0.3,0.3,0, 0,0,0, 0,0,0, 0,0,0]
else:
return None # bogus
g_jc.position = [0] * 12
if (spline_amount < 0):
spline_amount = 0
elif (spline_amount > 1):
spline_amount = 1
for i in xrange(0, 12):
target = origin[i] + g0[i] * x
prev_target = g_prev_jc_target.position[i]
#g_jc.position[i] = origin[i] + g0[i] * x
#delta = target - g_prev_jc_target.position[i]
# compute convex combination between old and new targets
g_jc.position[i] = ( spline_amount) * target + \
(1.0 - spline_amount) * prev_target
#print "joint state: %s" % (str(g_jc.position))
g_jc_pub.publish(g_jc)
if (spline_amount == 1.0):
for i in xrange(0, 12):
g_prev_jc_target.position[i] = g_jc.position[i] # todo: make this better
def grasp_cb(msg):
grasp_spline(msg.name, msg.closed_amount, 1)
if __name__ == '__main__':
rospy.init_node('simple_grasp')
g_jc.name = ["f0_j0", "f0_j1", "f0_j2",
"f1_j0", "f1_j1", "f1_j2",
"f2_j0", "f2_j1", "f2_j2",
"f3_j0", "f3_j1", "f3_j2"]
g_jc.position = [0] * 12
g_prev_jc_target.position = [0] * 12
g_jc_pub = rospy.Publisher('joint_commands', JointCommands, queue_size=1) # same namespace
g_jc_srv = rospy.Service('simple_grasp', SimpleGraspSrv, grasp_srv)
g_sgws_srv = rospy.Service('simple_grasp_with_slew', SimpleGraspWithSlew, grasp_slew_srv)
g_jc_sub = rospy.Subscriber('simple_grasp', SimpleGrasp, grasp_cb)
print "simple grasp service is now running."
rospy.spin()
| [] |
edzzn/Manejo_Liberia | ui/ui_prestamo_libros.py | c735d35b32fc53839acfc48d4e088e69983edf16 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'PrestamoDeLibros.ui'
#
# Created by: PyQt4 UI code generator 4.11.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName(_fromUtf8("Form"))
Form.resize(400, 300)
self.pushButton = QtGui.QPushButton(Form)
self.pushButton.setGeometry(QtCore.QRect(140, 70, 121, 41))
self.pushButton.setObjectName(_fromUtf8("pushButton"))
self.pushButton_2 = QtGui.QPushButton(Form)
self.pushButton_2.setGeometry(QtCore.QRect(140, 160, 121, 41))
self.pushButton_2.setObjectName(_fromUtf8("pushButton_2"))
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
Form.setWindowTitle(_translate("Form", "Form", None))
self.pushButton.setText(_translate("Form", "Solicitar", None))
self.pushButton_2.setText(_translate("Form", "Reservar", None))
if __name__ == "__main__":
import sys
app = QtGui.QApplication(sys.argv)
Form = QtGui.QWidget()
ui = Ui_Form()
ui.setupUi(Form)
Form.show()
sys.exit(app.exec_())
| [((47, 10, 47, 38), 'PyQt4.QtGui.QApplication', 'QtGui.QApplication', ({(47, 29, 47, 37): 'sys.argv'}, {}), '(sys.argv)', False, 'from PyQt4 import QtCore, QtGui\n'), ((48, 11, 48, 26), 'PyQt4.QtGui.QWidget', 'QtGui.QWidget', ({}, {}), '()', False, 'from PyQt4 import QtCore, QtGui\n'), ((20, 15, 20, 79), 'PyQt4.QtGui.QApplication.translate', 'QtGui.QApplication.translate', ({(20, 44, 20, 51): 'context', (20, 53, 20, 57): 'text', (20, 59, 20, 67): 'disambig', (20, 69, 20, 78): '_encoding'}, {}), '(context, text, disambig, _encoding)', False, 'from PyQt4 import QtCore, QtGui\n'), ((29, 26, 29, 49), 'PyQt4.QtGui.QPushButton', 'QtGui.QPushButton', ({(29, 44, 29, 48): 'Form'}, {}), '(Form)', False, 'from PyQt4 import QtCore, QtGui\n'), ((32, 28, 32, 51), 'PyQt4.QtGui.QPushButton', 'QtGui.QPushButton', ({(32, 46, 32, 50): 'Form'}, {}), '(Form)', False, 'from PyQt4 import QtCore, QtGui\n'), ((37, 8, 37, 51), 'PyQt4.QtCore.QMetaObject.connectSlotsByName', 'QtCore.QMetaObject.connectSlotsByName', ({(37, 46, 37, 50): 'Form'}, {}), '(Form)', False, 'from PyQt4 import QtCore, QtGui\n'), ((23, 15, 23, 68), 'PyQt4.QtGui.QApplication.translate', 'QtGui.QApplication.translate', ({(23, 44, 23, 51): 'context', (23, 53, 23, 57): 'text', (23, 59, 23, 67): 'disambig'}, {}), '(context, text, disambig)', False, 'from PyQt4 import QtCore, QtGui\n'), ((30, 36, 30, 66), 'PyQt4.QtCore.QRect', 'QtCore.QRect', ({(30, 49, 30, 52): '(140)', (30, 54, 30, 56): '(70)', (30, 58, 30, 61): '(121)', (30, 63, 30, 65): '(41)'}, {}), '(140, 70, 121, 41)', False, 'from PyQt4 import QtCore, QtGui\n'), ((33, 38, 33, 69), 'PyQt4.QtCore.QRect', 'QtCore.QRect', ({(33, 51, 33, 54): '(140)', (33, 56, 33, 59): '(160)', (33, 61, 33, 64): '(121)', (33, 66, 33, 68): '(41)'}, {}), '(140, 160, 121, 41)', False, 'from PyQt4 import QtCore, QtGui\n')] |
zopefoundation/zope.app.content | src/zope/app/content/__init__.py | d4c0276ff90bceed2156d808ab6b42b85d7b3810 | ##############################################################################
#
# Copyright (c) 2002 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Content Type convenience lookup functions."""
from zope.interface import provider
from zope.interface import providedBy
from zope.schema.interfaces import IVocabularyFactory
from zope.app.content.interfaces import IContentType
from zope.componentvocabulary.vocabulary import UtilityVocabulary
from zope.security.proxy import removeSecurityProxy
def queryType(object, interface):
"""Returns the object's interface which implements interface.
>>> from zope.interface import Interface
>>> class IContentType(Interface):
... pass
>>> from zope.interface import Interface, implementer, directlyProvides
>>> class I(Interface):
... pass
>>> class J(Interface):
... pass
>>> directlyProvides(I, IContentType)
>>> @implementer(I)
... class C(object):
... pass
>>> @implementer(J, I)
... class D(object):
... pass
>>> obj = C()
>>> c1_ctype = queryType(obj, IContentType)
>>> c1_ctype.__name__
'I'
>>> class I1(I):
... pass
>>> class I2(I1):
... pass
>>> class I3(Interface):
... pass
>>> @implementer(I1)
... class C1(object):
... pass
>>> obj1 = C1()
>>> c1_ctype = queryType(obj1, IContentType)
>>> c1_ctype.__name__
'I'
>>> @implementer(I2)
... class C2(object):
... pass
>>> obj2 = C2()
>>> c2_ctype = queryType(obj2, IContentType)
>>> c2_ctype.__name__
'I'
>>> @implementer(I3)
... class C3(object):
... pass
>>> obj3 = C3()
If Interface doesn't provide `IContentType`, `queryType` returns ``None``.
>>> c3_ctype = queryType(obj3, IContentType)
>>> c3_ctype
>>> c3_ctype is None
True
>>> class I4(I):
... pass
>>> directlyProvides(I4, IContentType)
>>> @implementer(I4)
... class C4(object):
... pass
>>> obj4 = C4()
>>> c4_ctype = queryType(obj4, IContentType)
>>> c4_ctype.__name__
'I4'
"""
# Remove the security proxy, so that we can introspect the type of the
# object's interfaces.
naked = removeSecurityProxy(object)
object_iro = providedBy(naked).__iro__
for iface in object_iro:
if interface.providedBy(iface):
return iface
return None
def queryContentType(object):
"""Returns the interface implemented by object which implements
:class:`zope.app.content.interfaces.IContentType`.
>>> from zope.interface import Interface, implementer, directlyProvides
>>> class I(Interface):
... pass
>>> directlyProvides(I, IContentType)
>>> @implementer(I)
... class C(object):
... pass
>>> obj = C()
>>> c1_ctype = queryContentType(obj)
>>> c1_ctype.__name__
'I'
"""
return queryType(object, IContentType)
@provider(IVocabularyFactory)
class ContentTypesVocabulary(UtilityVocabulary):
interface = IContentType
| [((129, 1, 129, 29), 'zope.interface.provider', 'provider', ({(129, 10, 129, 28): 'IVocabularyFactory'}, {}), '(IVocabularyFactory)', False, 'from zope.interface import provider\n'), ((99, 12, 99, 39), 'zope.security.proxy.removeSecurityProxy', 'removeSecurityProxy', ({(99, 32, 99, 38): 'object'}, {}), '(object)', False, 'from zope.security.proxy import removeSecurityProxy\n'), ((100, 17, 100, 34), 'zope.interface.providedBy', 'providedBy', ({(100, 28, 100, 33): 'naked'}, {}), '(naked)', False, 'from zope.interface import providedBy\n')] |
yschiebelhut/ewm-cloud-robotics | python-modules/robcoewmrobotconfigurator/robcoewmrobotconfigurator/run.py | bdf3a6c13850d266b70168912494300c32d4d803 | #!/usr/bin/env python3
# encoding: utf-8
#
# Copyright (c) 2019 SAP SE or an SAP affiliate company. All rights reserved.
#
# This file is part of ewm-cloud-robotics
# (see https://github.com/SAP/ewm-cloud-robotics).
#
# This file is licensed under the Apache Software License, v. 2 except as noted
# otherwise in the LICENSE file (https://github.com/SAP/ewm-cloud-robotics/blob/master/LICENSE)
#
"""Run the SAP EWM robot configurator."""
import sys
import signal
import traceback
import logging
import time
from robcoewmrobotconfigurator.ewm_robot_sync import EWMRobotSync
from robcoewmrobotconfigurator.robotconfigcontroller import RobotConfigurationController
from robcoewmrobotconfigurator.robco_robot_api import RobCoRobotAPI
_LOGGER = logging.getLogger(__name__)
class MainLoopController:
"""Control the main loop."""
def __init__(self):
"""Construct."""
# Shutdown Handler
self.shutdown = False
signal.signal(signal.SIGINT, self.exit_gracefully)
signal.signal(signal.SIGTERM, self.exit_gracefully)
# Sleep handler
self.last_time = time.time()
def exit_gracefully(self, signum, frame):
"""Set shutdown flag on SIGTERM and SIGINT."""
self.shutdown = True
_LOGGER.info('Closing application because signal %s received', signum)
def sleep(self, seconds: float):
"""Sleep maximum n seconds after the last call."""
timediff = time.time() - self.last_time
if timediff < seconds:
time.sleep(seconds-timediff)
self.last_time = time.time()
def run_robotconfigurator():
"""Run one instance of the robot configurator."""
# Register handler to control main loop
loop_control = MainLoopController()
# Create CR watcher instances
k8s_rb = RobCoRobotAPI()
k8s_rc = RobotConfigurationController()
# Create EWM robot syncer instance
robotsync = EWMRobotSync(k8s_rc)
# Register callback functions
k8s_rb.register_callback('ConfigurationController', ['ADDED'], k8s_rc.robco_robot_cb)
k8s_rc.register_callback(
'EWMRobotSync', ['ADDED', 'MODIFIED', 'REPROCESS'], robotsync.robotconfiguration_cb)
# Start
k8s_rb.run()
k8s_rc.run(reprocess=True)
_LOGGER.info('SAP EWM Robot Configurator started')
try:
# Looping while K8S watchers are running
while loop_control.shutdown is False:
# Refresh bearer token when using OAuth
if robotsync.odataconfig.authorization == robotsync.odataconfig.AUTH_OAUTH:
robotsync.odatahandler.refresh_access_token()
# Check if K8S CR handler exception occured
for k, exc in k8s_rb.thread_exceptions.items():
_LOGGER.error(
'Uncovered exception in "%s" thread of RobCoRobotAPI. Raising it in main '
'thread', k)
raise exc
for k, exc in k8s_rc.thread_exceptions.items():
_LOGGER.error(
'Uncovered exception in "%s" thread of RobotConfigurationController. Raising '
'it in main thread', k)
raise exc
# Sleep maximum 1.0 second
loop_control.sleep(1.0)
except KeyboardInterrupt:
_LOGGER.info('Keyboard interrupt - terminating')
except SystemExit:
_LOGGER.info('System exit - terminating')
finally:
# Stop K8S CR watchers
_LOGGER.info('Stopping K8S CR watchers')
k8s_rb.stop_watcher()
k8s_rc.stop_watcher()
# Shutdown threadpool executor
robotsync.executor.shutdown()
if __name__ == '__main__':
# Create root logger if running as main program
ROOT_LOGGER = logging.getLogger()
ROOT_LOGGER.setLevel(logging.INFO)
# Create console handler and set level to info
CH = logging.StreamHandler()
CH.setLevel(logging.INFO)
# Create formatter
FORMATTER = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# Add formatter to ch
CH.setFormatter(FORMATTER)
# Add ch to logger
ROOT_LOGGER.addHandler(CH)
# Run robot master
try:
run_robotconfigurator()
except Exception: # pylint: disable=broad-except
EXC_INFO = sys.exc_info()
_LOGGER.critical(
'Unexpected error "%s" - "%s" - TRACEBACK: %s', EXC_INFO[0], EXC_INFO[1],
traceback.format_exception(*EXC_INFO))
sys.exit('Application terminated with exception: "{}" - "{}"'.format(
EXC_INFO[0], EXC_INFO[1]))
| [((25, 10, 25, 37), 'logging.getLogger', 'logging.getLogger', ({(25, 28, 25, 36): '__name__'}, {}), '(__name__)', False, 'import logging\n'), ((61, 13, 61, 28), 'robcoewmrobotconfigurator.robco_robot_api.RobCoRobotAPI', 'RobCoRobotAPI', ({}, {}), '()', False, 'from robcoewmrobotconfigurator.robco_robot_api import RobCoRobotAPI\n'), ((62, 13, 62, 43), 'robcoewmrobotconfigurator.robotconfigcontroller.RobotConfigurationController', 'RobotConfigurationController', ({}, {}), '()', False, 'from robcoewmrobotconfigurator.robotconfigcontroller import RobotConfigurationController\n'), ((65, 16, 65, 36), 'robcoewmrobotconfigurator.ewm_robot_sync.EWMRobotSync', 'EWMRobotSync', ({(65, 29, 65, 35): 'k8s_rc'}, {}), '(k8s_rc)', False, 'from robcoewmrobotconfigurator.ewm_robot_sync import EWMRobotSync\n'), ((111, 18, 111, 37), 'logging.getLogger', 'logging.getLogger', ({}, {}), '()', False, 'import logging\n'), ((115, 9, 115, 32), 'logging.StreamHandler', 'logging.StreamHandler', ({}, {}), '()', False, 'import logging\n'), ((119, 16, 119, 89), 'logging.Formatter', 'logging.Formatter', ({(119, 34, 119, 88): '"""%(asctime)s - %(name)s - %(levelname)s - %(message)s"""'}, {}), "('%(asctime)s - %(name)s - %(levelname)s - %(message)s')", False, 'import logging\n'), ((35, 8, 35, 58), 'signal.signal', 'signal.signal', ({(35, 22, 35, 35): 'signal.SIGINT', (35, 37, 35, 57): 'self.exit_gracefully'}, {}), '(signal.SIGINT, self.exit_gracefully)', False, 'import signal\n'), ((36, 8, 36, 59), 'signal.signal', 'signal.signal', ({(36, 22, 36, 36): 'signal.SIGTERM', (36, 38, 36, 58): 'self.exit_gracefully'}, {}), '(signal.SIGTERM, self.exit_gracefully)', False, 'import signal\n'), ((38, 25, 38, 36), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((52, 25, 52, 36), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((47, 19, 47, 30), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((50, 12, 50, 40), 'time.sleep', 'time.sleep', ({(50, 23, 50, 39): '(seconds - timediff)'}, {}), '(seconds - timediff)', False, 'import time\n'), ((130, 19, 130, 33), 'sys.exc_info', 'sys.exc_info', ({}, {}), '()', False, 'import sys\n'), ((133, 12, 133, 49), 'traceback.format_exception', 'traceback.format_exception', ({(133, 39, 133, 48): '*EXC_INFO'}, {}), '(*EXC_INFO)', False, 'import traceback\n')] |
DanielSBrown/osf.io | website/addons/forward/views/__init__.py | 98dda2ac237377197acacce78274bc0a4ce8f303 | from . import config, widget # noqa
| [] |
crvallance/wlanpi-hwtest | hwtest/automated/usb3_test.py | 8858ef6e8fa78767238b968b121b4d5ab2155701 | from hwtest.shell_utils import run_command
def test_linux_usb3hub():
"""
Test for Linux Foundation 3.0 root hub in `lsusb` output
"""
resp = run_command(["lsusb"])
assert "1d6b:0003" in resp
| [((9, 11, 9, 33), 'hwtest.shell_utils.run_command', 'run_command', ({(9, 23, 9, 32): "['lsusb']"}, {}), "(['lsusb'])", False, 'from hwtest.shell_utils import run_command\n')] |
tp-m/meson | ninjabackend.py | 2d1aa395e86848ca948d30d83cc5357777e5b490 | # Copyright 2012-2014 The Meson development team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import backends
import environment, mesonlib
import build
import mlog
import dependencies
from mesonlib import File
from meson_install import InstallData
from build import InvalidArguments
from coredata import MesonException
import os, sys, pickle, re
import subprocess, shutil
if mesonlib.is_windows():
quote_char = '"'
execute_wrapper = 'cmd /c'
else:
quote_char = "'"
execute_wrapper = ''
def ninja_quote(text):
return text.replace(' ', '$ ').replace(':', '$:')
class RawFilename():
def __init__(self, fname):
self.fname = fname
def split(self, c):
return self.fname.split(c)
def startswith(self, s):
return self.fname.startswith(s)
class NinjaBuildElement():
def __init__(self, outfilenames, rule, infilenames):
if isinstance(outfilenames, str):
self.outfilenames = [outfilenames]
else:
self.outfilenames = outfilenames
assert(isinstance(rule, str))
self.rule = rule
if isinstance(infilenames, str):
self.infilenames = [infilenames]
else:
self.infilenames = infilenames
self.deps = []
self.orderdeps = []
self.elems = []
def add_dep(self, dep):
if isinstance(dep, list):
self.deps += dep
else:
self.deps.append(dep)
def add_orderdep(self, dep):
if isinstance(dep, list):
self.orderdeps += dep
else:
self.orderdeps.append(dep)
def add_item(self, name, elems):
if isinstance(elems, str):
elems = [elems]
self.elems.append((name, elems))
def write(self, outfile):
line = 'build %s: %s %s' % (' '.join([ninja_quote(i) for i in self.outfilenames]),\
self.rule,
' '.join([ninja_quote(i) for i in self.infilenames]))
if len(self.deps) > 0:
line += ' | ' + ' '.join([ninja_quote(x) for x in self.deps])
if len(self.orderdeps) > 0:
line += ' || ' + ' '.join([ninja_quote(x) for x in self.orderdeps])
line += '\n'
# This is the only way I could find to make this work on all
# platforms including Windows command shell. Slash is a dir separator
# on Windows, too, so all characters are unambiguous and, more importantly,
# do not require quoting.
line = line.replace('\\', '/')
outfile.write(line)
for e in self.elems:
(name, elems) = e
should_quote = True
if name == 'DEPFILE' or name == 'DESC' or name == 'pool':
should_quote = False
line = ' %s = ' % name
q_templ = quote_char + "%s" + quote_char
noq_templ = "%s"
newelems = []
for i in elems:
if not should_quote or i == '&&': # Hackety hack hack
templ = noq_templ
else:
templ = q_templ
i = i.replace('\\', '\\\\')
if quote_char == '"':
i = i.replace('"', '\\"')
newelems.append(templ % ninja_quote(i))
line += ' '.join(newelems)
line += '\n'
outfile.write(line)
outfile.write('\n')
class NinjaBackend(backends.Backend):
def __init__(self, build):
super().__init__(build)
self.source_suffix_in_objs = True
self.ninja_filename = 'build.ninja'
self.fortran_deps = {}
self.all_outputs = {}
def check_outputs(self, elem):
for n in elem.outfilenames:
if n in self.all_outputs:
raise MesonException('Multiple producers for Ninja target "%s". Please rename your targets.' % n)
self.all_outputs[n] = True
def detect_vs_dep_prefix(self, outfile, tempfilename):
'''VS writes its dependency in a locale dependent format.
Detect the search prefix to use.'''
if shutil.which('cl') is None:
return outfile
outfile.close()
open(os.path.join(self.environment.get_scratch_dir(), 'incdetect.c'),
'w').write('''#include<stdio.h>
int dummy;
''')
pc = subprocess.Popen(['cl', '/showIncludes', '/c', 'incdetect.c'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=self.environment.get_scratch_dir())
(stdo, _) = pc.communicate()
for line in stdo.split(b'\r\n'):
if line.endswith(b'stdio.h'):
matchstr = b':'.join(line.split(b':')[0:2]) + b':'
binfile = open(tempfilename, 'ab')
binfile.write(b'msvc_deps_prefix = ' + matchstr + b'\r\n')
binfile.close()
return open(tempfilename, 'a')
raise MesonException('Could not determine vs dep dependency prefix string.')
def generate(self, interp):
self.interpreter = interp
outfilename = os.path.join(self.environment.get_build_dir(), self.ninja_filename)
tempfilename = outfilename + '~'
outfile = open(tempfilename, 'w')
outfile.write('# This is the build file for project "%s"\n' % self.build.get_project())
outfile.write('# It is autogenerated by the Meson build system.\n')
outfile.write('# Do not edit by hand.\n\n')
outfile.write('ninja_required_version = 1.5.1\n\n')
outfile = self.detect_vs_dep_prefix(outfile, tempfilename)
self.generate_rules(outfile)
self.generate_phony(outfile)
outfile.write('# Build rules for targets\n\n')
[self.generate_target(t, outfile) for t in self.build.get_targets().values()]
if len(self.build.pot) > 0:
outfile.write('# Build rules for localisation.\n\n')
self.generate_po(outfile)
outfile.write('# Test rules\n\n')
self.generate_tests(outfile)
outfile.write('# Install rules\n\n')
self.generate_install(outfile)
if self.environment.coredata.get_builtin_option('coverage'):
outfile.write('# Coverage rules\n\n')
self.generate_coverage_rules(outfile)
outfile.write('# Suffix\n\n')
self.generate_ending(outfile)
# Only ovewrite the old build file after the new one has been
# fully created.
outfile.close()
os.replace(tempfilename, outfilename)
self.generate_compdb()
# http://clang.llvm.org/docs/JSONCompilationDatabase.html
def generate_compdb(self):
ninja_exe = environment.detect_ninja()
builddir = self.environment.get_build_dir()
jsondb = subprocess.check_output([ninja_exe, '-t', 'compdb', 'c_COMPILER', 'cpp_COMPILER'], cwd=builddir)
open(os.path.join(builddir, 'compile_commands.json'), 'wb').write(jsondb)
# Get all generated headers. Any source file might need them so
# we need to add an order dependency to them.
def get_generated_headers(self, target):
header_deps = []
for gensource in target.get_generated_sources():
if isinstance(gensource, build.CustomTarget):
continue
for src in gensource.get_outfilelist():
if self.environment.is_header(src):
header_deps.append(os.path.join(self.get_target_private_dir(target), src))
for dep in target.link_targets:
if isinstance(dep, (build.StaticLibrary, build.SharedLibrary)):
header_deps += self.get_generated_headers(dep)
return header_deps
def generate_target(self, target, outfile):
if isinstance(target, build.CustomTarget):
self.generate_custom_target(target, outfile)
if isinstance(target, build.RunTarget):
self.generate_run_target(target, outfile)
name = target.get_id()
gen_src_deps = []
if name in self.processed_targets:
return
if isinstance(target, build.Jar):
self.generate_jar_target(target, outfile)
return
if 'rust' in self.environment.coredata.compilers.keys() and self.has_rust(target):
self.generate_rust_target(target, outfile)
return
if 'cs' in self.environment.coredata.compilers.keys() and self.has_cs(target):
self.generate_cs_target(target, outfile)
return
if 'vala' in self.environment.coredata.compilers.keys() and self.has_vala(target):
gen_src_deps += self.generate_vala_compile(target, outfile)
if 'swift' in self.environment.coredata.compilers.keys() and self.has_swift(target):
self.generate_swift_target(target, outfile)
return
self.scan_fortran_module_outputs(target)
# The following deals with C/C++ compilation.
(gen_src, gen_other_deps) = self.process_dep_gens(outfile, target)
gen_src_deps += gen_src
self.process_target_dependencies(target, outfile)
self.generate_custom_generator_rules(target, outfile)
outname = self.get_target_filename(target)
obj_list = []
use_pch = self.environment.coredata.get_builtin_option('use_pch')
is_unity = self.environment.coredata.get_builtin_option('unity')
if use_pch and target.has_pch():
pch_objects = self.generate_pch(target, outfile)
else:
pch_objects = []
header_deps = gen_other_deps
unity_src = []
unity_deps = [] # Generated sources that must be built before compiling a Unity target.
header_deps += self.get_generated_headers(target)
for gensource in target.get_generated_sources():
if isinstance(gensource, build.CustomTarget):
for src in gensource.output:
src = os.path.join(self.get_target_dir(gensource), src)
if self.environment.is_source(src) and not self.environment.is_header(src):
if is_unity:
unity_deps.append(os.path.join(self.environment.get_build_dir(), RawFilename(src)))
else:
obj_list.append(self.generate_single_compile(target, outfile, RawFilename(src), True,
header_deps))
elif self.environment.is_object(src):
obj_list.append(src)
elif self.environment.is_library(src):
pass
else:
# Assume anything not specifically a source file is a header. This is because
# people generate files with weird suffixes (.inc, .fh) that they then include
# in their source files.
header_deps.append(RawFilename(src))
else:
for src in gensource.get_outfilelist():
if self.environment.is_object(src):
obj_list.append(os.path.join(self.get_target_private_dir(target), src))
elif not self.environment.is_header(src):
if is_unity:
if self.has_dir_part(src):
rel_src = src
else:
rel_src = os.path.join(self.get_target_private_dir(target), src)
unity_deps.append(rel_src)
abs_src = os.path.join(self.environment.get_build_dir(), rel_src)
unity_src.append(abs_src)
else:
obj_list.append(self.generate_single_compile(target, outfile, src, True,
header_deps=header_deps))
src_list = []
for src in gen_src_deps:
src_list.append(src)
if is_unity:
unity_src.append(os.path.join(self.environment.get_build_dir(), src))
header_deps.append(src)
else:
# Generated targets are ordered deps because the must exist
# before the sources compiling them are used. After the first
# compile we get precise dependency info from dep files.
# This should work in all cases. If it does not, then just
# move them from orderdeps to proper deps.
if self.environment.is_header(src):
header_deps.append(src)
else:
obj_list.append(self.generate_single_compile(target, outfile, src, True, [], header_deps))
for src in target.get_sources():
if src.endswith('.vala'):
continue
if not self.environment.is_header(src):
src_list.append(src)
if is_unity:
abs_src = os.path.join(self.environment.get_build_dir(),
src.rel_to_builddir(self.build_to_src))
unity_src.append(abs_src)
else:
obj_list.append(self.generate_single_compile(target, outfile, src, False, [], header_deps))
obj_list += self.flatten_object_list(target)
if is_unity:
for src in self.generate_unity_files(target, unity_src):
obj_list.append(self.generate_single_compile(target, outfile, src, True, unity_deps + header_deps))
linker = self.determine_linker(target, src_list)
elem = self.generate_link(target, outfile, outname, obj_list, linker, pch_objects)
self.generate_shlib_aliases(target, self.get_target_dir(target))
elem.write(outfile)
self.processed_targets[name] = True
def process_target_dependencies(self, target, outfile):
for t in target.get_dependencies():
tname = t.get_basename() + t.type_suffix()
if not tname in self.processed_targets:
self.generate_target(t, outfile)
def generate_custom_target(self, target, outfile):
(srcs, ofilenames, cmd) = self.eval_custom_target_command(target)
deps = []
for i in target.get_dependencies():
# FIXME, should not grab element at zero but rather expand all.
if isinstance(i, list):
i = i[0]
fname = i.get_filename()
if isinstance(fname, list):
fname = fname[0]
deps.append(os.path.join(self.get_target_dir(i), fname))
if target.build_always:
deps.append('PHONY')
elem = NinjaBuildElement(ofilenames, 'CUSTOM_COMMAND', srcs)
for i in target.depend_files:
if isinstance(i, mesonlib.File):
deps.append(i.rel_to_builddir(self.build_to_src))
else:
deps.append(os.path.join(self.build_to_src, i))
elem.add_dep(deps)
for d in target.extra_depends:
tmp = d.get_filename()
if not isinstance(tmp, list):
tmp = [tmp]
for fname in tmp:
elem.add_dep(os.path.join(self.get_target_dir(d), fname))
elem.add_item('COMMAND', cmd)
elem.add_item('description', 'Generating %s with a custom command.' % target.name)
elem.write(outfile)
self.check_outputs(elem)
self.processed_targets[target.name + target.type_suffix()] = True
def generate_run_target(self, target, outfile):
runnerscript = os.path.join(self.environment.get_script_dir(), 'commandrunner.py')
deps = []
arg_strings = []
for i in target.args:
if isinstance(i, str):
arg_strings.append(i)
elif isinstance(i, (build.BuildTarget, build.CustomTarget)):
relfname = self.get_target_filename(i)
deps.append(relfname)
arg_strings.append(os.path.join(self.environment.get_build_dir(), relfname))
else:
mlog.debug(str(i))
raise MesonException('Unreachable code in generate_run_target.')
elem = NinjaBuildElement(target.name, 'CUSTOM_COMMAND', deps)
cmd = [sys.executable, runnerscript, self.environment.get_source_dir(), self.environment.get_build_dir(), target.subdir]
texe = target.command
try:
texe = texe.held_object
except AttributeError:
pass
if isinstance(texe, build.Executable):
abs_exe = os.path.join(self.environment.get_build_dir(), self.get_target_filename(texe))
deps.append(self.get_target_filename(texe))
if self.environment.is_cross_build() \
and self.environment.cross_info.config['binaries'].get('exe_wrapper', None) is not None:
cmd += [self.environment.cross_info.config['binaries']['exe_wrapper']]
cmd.append(abs_exe)
else:
cmd.append(target.command)
cmd += arg_strings
elem.add_item('COMMAND', cmd)
elem.add_item('description', 'Running external command %s.' % target.name)
elem.add_item('pool', 'console')
elem.write(outfile)
self.check_outputs(elem)
self.processed_targets[target.name + target.type_suffix()] = True
def generate_po(self, outfile):
for p in self.build.pot:
(packagename, languages, subdir) = p
input_file = os.path.join(subdir, 'POTFILES')
elem = NinjaBuildElement('pot', 'GEN_POT', [])
elem.add_item('PACKAGENAME', packagename)
elem.add_item('OUTFILE', packagename + '.pot')
elem.add_item('FILELIST', os.path.join(self.environment.get_source_dir(), input_file))
elem.add_item('OUTDIR', os.path.join(self.environment.get_source_dir(), subdir))
elem.write(outfile)
self.check_outputs(elem)
for l in languages:
infile = os.path.join(self.environment.get_source_dir(), subdir, l + '.po')
outfilename = os.path.join(subdir, l + '.gmo')
lelem = NinjaBuildElement(outfilename, 'GEN_GMO', infile)
lelem.add_item('INFILE', infile)
lelem.add_item('OUTFILE', outfilename)
lelem.write(outfile)
self.check_outputs(lelem)
def generate_coverage_rules(self, outfile):
(gcovr_exe, lcov_exe, genhtml_exe) = environment.find_coverage_tools()
added_rule = False
if gcovr_exe:
added_rule = True
elem = NinjaBuildElement('coverage-xml', 'CUSTOM_COMMAND', '')
elem.add_item('COMMAND', [gcovr_exe, '-x', '-r', self.environment.get_build_dir(),\
'-o', os.path.join(self.environment.get_log_dir(), 'coverage.xml')])
elem.add_item('DESC', 'Generating XML coverage report.')
elem.write(outfile)
elem = NinjaBuildElement('coverage-text', 'CUSTOM_COMMAND', '')
elem.add_item('COMMAND', [gcovr_exe, '-r', self.environment.get_build_dir(),\
'-o', os.path.join(self.environment.get_log_dir(), 'coverage.txt')])
elem.add_item('DESC', 'Generating text coverage report.')
elem.write(outfile)
self.check_outputs(elem)
if lcov_exe and genhtml_exe:
added_rule = True
phony_elem = NinjaBuildElement('coverage-html', 'phony', 'coveragereport/index.html')
phony_elem.write(outfile)
elem = NinjaBuildElement('coveragereport/index.html', 'CUSTOM_COMMAND', '')
command = [lcov_exe, '--directory', self.environment.get_build_dir(),\
'--capture', '--output-file', 'coverage.info', '--no-checksum',\
'&&', genhtml_exe, '--prefix', self.environment.get_build_dir(),\
'--output-directory', self.environment.get_log_dir(), '--title', 'Code coverage',\
'--legend', '--show-details', 'coverage.info']
elem.add_item('COMMAND', command)
elem.add_item('DESC', 'Generating HTML coverage report.')
self.check_outputs(elem)
elem.write(outfile)
if not added_rule:
mlog.log(mlog.red('Warning:'), 'coverage requested but neither gcovr nor lcov/genhtml found.')
def generate_install(self, outfile):
script_root = self.environment.get_script_dir()
install_script = os.path.join(script_root, 'meson_install.py')
install_data_file = os.path.join(self.environment.get_scratch_dir(), 'install.dat')
depfixer = os.path.join(script_root, 'depfixer.py')
d = InstallData(self.environment.get_source_dir(),
self.environment.get_build_dir(),
self.environment.get_prefix(), depfixer)
elem = NinjaBuildElement('install', 'CUSTOM_COMMAND', 'PHONY')
elem.add_dep('all')
elem.add_item('DESC', 'Installing files.')
elem.add_item('COMMAND', [sys.executable, install_script, install_data_file])
elem.add_item('pool', 'console')
self.generate_depmf_install(d)
self.generate_target_install(d)
self.generate_header_install(d)
self.generate_man_install(d)
self.generate_data_install(d)
self.generate_po_install(d, elem)
self.generate_custom_install_script(d)
self.generate_subdir_install(d)
elem.write(outfile)
self.check_outputs(elem)
ofile = open(install_data_file, 'wb')
pickle.dump(d, ofile)
def generate_po_install(self, d, elem):
for p in self.build.pot:
(package_name, languages, subdir) = p
# FIXME: assumes only one po package per source
d.po_package_name = package_name
for lang in languages:
rel_src = os.path.join(subdir, lang + '.gmo')
src_file = os.path.join(self.environment.get_build_dir(), rel_src)
d.po.append((src_file, self.environment.coredata.get_builtin_option('localedir'), lang))
elem.add_dep(rel_src)
def generate_target_install(self, d):
libdir = self.environment.get_libdir()
bindir = self.environment.get_bindir()
should_strip = self.environment.coredata.get_builtin_option('strip')
for t in self.build.get_targets().values():
if t.should_install():
outdir = t.get_custom_install_dir()
if outdir is None:
if isinstance(t, build.Executable):
outdir = bindir
else:
outdir = libdir
i = [self.get_target_filename(t), outdir, t.get_aliaslist(),\
should_strip, t.install_rpath]
d.targets.append(i)
def generate_custom_install_script(self, d):
d.install_scripts = self.build.install_scripts
def generate_header_install(self, d):
incroot = self.environment.get_includedir()
headers = self.build.get_headers()
for h in headers:
outdir = h.get_custom_install_dir()
if outdir is None:
outdir = os.path.join(incroot, h.get_install_subdir())
for f in h.get_sources():
abspath = os.path.join(self.environment.get_source_dir(), h.get_source_subdir(), f)
i = [abspath, outdir]
d.headers.append(i)
def generate_man_install(self, d):
manroot = self.environment.get_mandir()
man = self.build.get_man()
for m in man:
for f in m.get_sources():
num = f.split('.')[-1]
subdir = m.get_custom_install_dir()
if subdir is None:
subdir = os.path.join(manroot, 'man' + num)
srcabs = os.path.join(self.environment.get_source_dir(), m.get_source_subdir(), f)
dstabs = os.path.join(subdir, f + '.gz')
i = [srcabs, dstabs]
d.man.append(i)
def generate_data_install(self, d):
data = self.build.get_data()
for de in data:
assert(isinstance(de, build.Data))
subdir = de.install_dir
for f in de.sources:
if de.in_sourcetree:
srcprefix = self.environment.get_source_dir()
else:
srcprefix = self.environment.get_build_dir()
srcabs = os.path.join(srcprefix, de.source_subdir, f)
dstabs = os.path.join(subdir, f)
i = [srcabs, dstabs]
d.data.append(i)
def generate_subdir_install(self, d):
for sd in self.build.get_install_subdirs():
src_dir = os.path.join(self.environment.get_source_dir(), sd.source_subdir, sd.installable_subdir)
dst_dir = os.path.join(self.environment.get_prefix(), sd.install_dir)
d.install_subdirs.append([src_dir, dst_dir])
def write_test_suite_targets(self, cmd, outfile):
suites = {}
for t in self.build.get_tests():
for s in t.suite:
suites[s] = True
suites = list(suites.keys())
suites.sort()
for s in suites:
if s == '':
visible_name = 'for top level tests'
else:
visible_name = s
elem = NinjaBuildElement('test-' + s, 'CUSTOM_COMMAND', ['all', 'PHONY'])
elem.add_item('COMMAND', cmd + ['--suite=' + s])
elem.add_item('DESC', 'Running test suite %s.' % visible_name)
elem.add_item('pool', 'console')
elem.write(outfile)
self.check_outputs(elem)
def generate_tests(self, outfile):
self.serialise_tests()
valgrind = environment.find_valgrind()
script_root = self.environment.get_script_dir()
test_script = os.path.join(script_root, 'meson_test.py')
test_data = os.path.join(self.environment.get_scratch_dir(), 'meson_test_setup.dat')
cmd = [sys.executable, test_script, test_data]
elem = NinjaBuildElement('test', 'CUSTOM_COMMAND', ['all', 'PHONY'])
elem.add_item('COMMAND', cmd)
elem.add_item('DESC', 'Running all tests.')
elem.add_item('pool', 'console')
elem.write(outfile)
self.check_outputs(elem)
self.write_test_suite_targets(cmd, outfile)
if valgrind:
velem = NinjaBuildElement('test-valgrind', 'CUSTOM_COMMAND', ['all', 'PHONY'])
velem.add_item('COMMAND', cmd + ['--wrapper=' + valgrind])
velem.add_item('DESC', 'Running test suite under Valgrind.')
velem.add_item('pool', 'console')
velem.write(outfile)
self.check_outputs(velem)
# And then benchmarks.
benchmark_script = os.path.join(script_root, 'meson_benchmark.py')
benchmark_data = os.path.join(self.environment.get_scratch_dir(), 'meson_benchmark_setup.dat')
cmd = [sys.executable, benchmark_script, benchmark_data]
elem = NinjaBuildElement('benchmark', 'CUSTOM_COMMAND', ['all', 'PHONY'])
elem.add_item('COMMAND', cmd)
elem.add_item('DESC', 'Running benchmark suite.')
elem.add_item('pool', 'console')
elem.write(outfile)
self.check_outputs(elem)
def generate_rules(self, outfile):
outfile.write('# Rules for compiling.\n\n')
self.generate_compile_rules(outfile)
outfile.write('# Rules for linking.\n\n')
if self.environment.is_cross_build():
self.generate_static_link_rules(True, outfile)
self.generate_static_link_rules(False, outfile)
self.generate_dynamic_link_rules(outfile)
outfile.write('# Other rules\n\n')
outfile.write('rule CUSTOM_COMMAND\n')
outfile.write(' command = $COMMAND\n')
outfile.write(' description = $DESC\n')
outfile.write(' restat = 1\n\n')
outfile.write('rule REGENERATE_BUILD\n')
c = (quote_char + ninja_quote(sys.executable) + quote_char,
quote_char + ninja_quote(self.environment.get_build_command()) + quote_char,
quote_char + ninja_quote(self.environment.get_source_dir()) + quote_char,
quote_char + ninja_quote(self.environment.get_build_dir()) + quote_char)
outfile.write(" command = %s %s %s %s --backend ninja secret-handshake\n" % c)
outfile.write(' description = Regenerating build files\n')
outfile.write(' generator = 1\n\n')
if len(self.build.pot) > 0:
self.generate_gettext_rules(outfile)
outfile.write('\n')
def generate_gettext_rules(self, outfile):
rule = 'rule GEN_POT\n'
command = " command = xgettext --package-name=$PACKAGENAME -p $OUTDIR -f $FILELIST -D '%s' -k_ -o $OUTFILE\n" % \
self.environment.get_source_dir()
desc = " description = Creating pot file for package $PACKAGENAME.\n"
outfile.write(rule)
outfile.write(command)
outfile.write(desc)
outfile.write('\n')
rule = 'rule GEN_GMO\n'
command = ' command = msgfmt $INFILE -o $OUTFILE\n'
desc = ' description = Generating gmo file $OUTFILE\n'
outfile.write(rule)
outfile.write(command)
outfile.write(desc)
outfile.write('\n')
def generate_phony(self, outfile):
outfile.write('# Phony build target, always out of date\n')
outfile.write('build PHONY: phony\n')
outfile.write('\n')
def generate_jar_target(self, target, outfile):
fname = target.get_filename()
subdir = target.get_subdir()
outname_rel = os.path.join(self.get_target_dir(target), fname)
src_list = target.get_sources()
class_list = []
compiler = self.get_compiler_for_source(src_list[0])
assert(compiler.get_language() == 'java')
c = 'c'
m = ''
e = ''
f = 'f'
main_class = target.get_main_class()
if main_class != '':
e = 'e'
for src in src_list:
plain_class_path = self.generate_single_java_compile(src, target, compiler, outfile)
class_list.append(plain_class_path)
class_dep_list = [os.path.join(self.get_target_private_dir(target), i) for i in class_list]
jar_rule = 'java_LINKER'
commands = [c+m+e+f]
if e != '':
commands.append(main_class)
commands.append(self.get_target_filename(target))
for cls in class_list:
commands += ['-C', self.get_target_private_dir(target), cls]
elem = NinjaBuildElement(outname_rel, jar_rule, [])
elem.add_dep(class_dep_list)
elem.add_item('ARGS', commands)
elem.write(outfile)
self.check_outputs(elem)
def generate_cs_resource_tasks(self, target, outfile):
args = []
deps = []
for r in target.resources:
rel_sourcefile = os.path.join(self.build_to_src, target.subdir, r)
if r.endswith('.resources'):
a = '-resource:' + rel_sourcefile
elif r.endswith('.txt') or r.endswith('.resx'):
ofilebase = os.path.splitext(os.path.basename(r))[0] + '.resources'
ofilename = os.path.join(self.get_target_private_dir(target), ofilebase)
elem = NinjaBuildElement(ofilename, "CUSTOM_COMMAND", rel_sourcefile)
elem.add_item('COMMAND', ['resgen', rel_sourcefile, ofilename])
elem.add_item('DESC', 'Compiling resource %s.' % rel_sourcefile)
elem.write(outfile)
self.check_outputs(elem)
deps.append(ofilename)
a = '-resource:' + ofilename
else:
raise InvalidArguments('Unknown resource file %s.' % r)
args.append(a)
return (args, deps)
def generate_cs_target(self, target, outfile):
buildtype = self.environment.coredata.get_builtin_option('buildtype')
fname = target.get_filename()
outname_rel = os.path.join(self.get_target_dir(target), fname)
src_list = target.get_sources()
compiler = self.get_compiler_for_source(src_list[0])
assert(compiler.get_language() == 'cs')
rel_srcs = [s.rel_to_builddir(self.build_to_src) for s in src_list]
deps = []
commands = target.extra_args.get('cs', [])
commands += compiler.get_buildtype_args(buildtype)
if isinstance(target, build.Executable):
commands.append('-target:exe')
elif isinstance(target, build.SharedLibrary):
commands.append('-target:library')
else:
raise MesonException('Unknown C# target type.')
(resource_args, resource_deps) = self.generate_cs_resource_tasks(target, outfile)
commands += resource_args
deps += resource_deps
commands += compiler.get_output_args(outname_rel)
for l in target.link_targets:
lname = os.path.join(self.get_target_dir(l), l.get_filename())
commands += compiler.get_link_args(lname)
deps.append(lname)
if '-g' in commands:
outputs = [outname_rel, outname_rel + '.mdb']
else:
outputs = [outname_rel]
elem = NinjaBuildElement(outputs, 'cs_COMPILER', rel_srcs)
elem.add_dep(deps)
elem.add_item('ARGS', commands)
self.check_outputs(elem)
elem.write(outfile)
def generate_single_java_compile(self, src, target, compiler, outfile):
args = []
args += compiler.get_buildtype_args(self.environment.coredata.get_builtin_option('buildtype'))
args += compiler.get_output_args(self.get_target_private_dir(target))
for i in target.include_dirs:
for idir in i.get_incdirs():
args += ['-sourcepath', os.path.join(self.build_to_src, i.curdir, idir)]
rel_src = src.rel_to_builddir(self.build_to_src)
plain_class_path = src.fname[:-4] + 'class'
rel_obj = os.path.join(self.get_target_private_dir(target), plain_class_path)
element = NinjaBuildElement(rel_obj, compiler.get_language() + '_COMPILER', rel_src)
element.add_item('ARGS', args)
element.write(outfile)
self.check_outputs(element)
return plain_class_path
def generate_java_link(self, outfile):
rule = 'rule java_LINKER\n'
command = ' command = jar $ARGS\n'
description = ' description = Creating jar $out.\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write('\n')
def split_vala_sources(self, sources):
src = []
vapi_src = []
for s in sources:
if s.endswith('.vapi'):
vapi_src.append(s)
else:
src.append(s)
return (src, vapi_src)
def determine_dep_vapis(self, target):
result = []
for dep in target.link_targets:
for i in dep.sources:
if hasattr(i, 'fname'):
i = i.fname
if i.endswith('vala'):
vapiname = os.path.splitext(os.path.split(i)[1])[0] + '.vapi'
fullname = os.path.join(self.get_target_private_dir(dep), vapiname)
result.append(fullname)
break
return result
def generate_vala_compile(self, target, outfile):
"""Vala is compiled into C. Set up all necessary build steps here."""
valac = self.environment.coredata.compilers['vala']
(src, vapi_src) = self.split_vala_sources(target.get_sources())
vapi_src = [x.rel_to_builddir(self.build_to_src) for x in vapi_src]
extra_dep_files = []
vala_input_files = []
for s in src:
if s.endswith('.vala'):
vala_input_files.append(s.rel_to_builddir(self.build_to_src))
namebase = os.path.splitext(os.path.split(vala_input_files[0])[1])[0]
hname = namebase + '.h'
vapiname = namebase + '.vapi'
outputs = [vapiname]
args = ['-d', self.get_target_private_dir(target)]
args += ['-C']#, '-o', cname]
if not isinstance(target, build.Executable):
outputs.append(hname)
args += ['-H', hname]
args += ['--vapi=' + vapiname]
for src in vala_input_files:
namebase = os.path.splitext(os.path.split(src)[1])[0] + '.c'
outputs.append(namebase)
if self.environment.coredata.get_builtin_option('werror'):
args += valac.get_werror_args()
for d in target.external_deps:
if isinstance(d, dependencies.PkgConfigDependency):
if d.name == 'glib-2.0' and d.version_requirement is not None \
and d.version_requirement.startswith(('>=', '==')):
args += ['--target-glib', d.version_requirement[2:]]
args += ['--pkg', d.name]
extra_args = []
for a in target.extra_args.get('vala', []):
if isinstance(a, File):
relname = a.rel_to_builddir(self.build_to_src)
extra_dep_files.append(relname)
extra_args.append(relname)
else:
extra_args.append(a)
dependency_vapis = self.determine_dep_vapis(target)
extra_dep_files += dependency_vapis
args += extra_args
args += dependency_vapis
outputs = [os.path.join(self.get_target_private_dir(target), x) for x in outputs]
element = NinjaBuildElement(outputs,
valac.get_language() + '_COMPILER',
vala_input_files + vapi_src)
element.add_item('ARGS', args)
element.add_dep(extra_dep_files)
element.write(outfile)
self.check_outputs(element)
return outputs
def generate_rust_target(self, target, outfile):
rustc = self.environment.coredata.compilers['rust']
relsrc = []
for i in target.get_sources():
if not rustc.can_compile(i):
raise InvalidArguments('Rust target %s contains a non-rust source file.' % target.get_basename())
relsrc.append(i.rel_to_builddir(self.build_to_src))
target_name = os.path.join(target.subdir, target.get_filename())
args = ['--crate-type']
if isinstance(target, build.Executable):
cratetype = 'bin'
elif isinstance(target, build.SharedLibrary):
cratetype = 'rlib'
elif isinstance(target, build.StaticLibrary):
cratetype = 'rlib'
else:
raise InvalidArguments('Unknown target type for rustc.')
args.append(cratetype)
args += rustc.get_buildtype_args(self.environment.coredata.get_builtin_option('buildtype'))
depfile = target.name + '.d'
args += ['--out-dir', target.subdir]
args += ['--emit', 'dep-info', '--emit', 'link']
orderdeps = [os.path.join(t.subdir, t.get_filename()) for t in target.link_targets]
linkdirs = {}
for d in target.link_targets:
linkdirs[d.subdir] = True
for d in linkdirs.keys():
if d == '':
d = '.'
args += ['-L', d]
element = NinjaBuildElement(target_name, 'rust_COMPILER', relsrc)
if len(orderdeps) > 0:
element.add_orderdep(orderdeps)
element.add_item('ARGS', args)
element.add_item('targetdep', depfile)
element.add_item('cratetype', cratetype)
element.write(outfile)
self.check_outputs(element)
def swift_module_file_name(self, target):
return os.path.join(self.get_target_private_dir(target),
self.target_swift_modulename(target) + '.swiftmodule')
def target_swift_modulename(self, target):
return target.name
def is_swift_target(self, target):
for s in target.sources:
if s.endswith('swift'):
return True
return False
def determine_swift_dep_modules(self, target):
result = []
for l in target.link_targets:
if self.is_swift_target(l):
result.append(self.swift_module_file_name(l))
return result
def determine_swift_dep_dirs(self, target):
result = []
for l in target.link_targets:
result.append(self.get_target_private_dir_abs(l))
return result
def get_swift_link_deps(self, target):
result = []
for l in target.link_targets:
result.append(self.get_target_filename(l))
return result
def split_swift_generated_sources(self, target):
all_srcs = []
for genlist in target.get_generated_sources():
if isinstance(genlist, build.CustomTarget):
for ifile in genlist.get_filename():
rel = os.path.join(self.get_target_dir(genlist), ifile)
all_srcs.append(rel)
else:
for ifile in genlist.get_outfilelist():
rel = os.path.join(self.get_target_private_dir(target), ifile)
all_srcs.append(rel)
srcs = []
others = []
for i in all_srcs:
if i.endswith('.swift'):
srcs.append(i)
else:
others.append(i)
return (srcs, others)
def generate_swift_target(self, target, outfile):
module_name = self.target_swift_modulename(target)
swiftc = self.environment.coredata.compilers['swift']
abssrc = []
abs_headers = []
header_imports = []
for i in target.get_sources():
if swiftc.can_compile(i):
relsrc = i.rel_to_builddir(self.build_to_src)
abss = os.path.normpath(os.path.join(self.environment.get_build_dir(), relsrc))
abssrc.append(abss)
elif self.environment.is_header(i):
relh = i.rel_to_builddir(self.build_to_src)
absh = os.path.normpath(os.path.join(self.environment.get_build_dir(), relh))
abs_headers.append(absh)
header_imports += swiftc.get_header_import_args(absh)
else:
raise InvalidArguments('Swift target %s contains a non-swift source file.' % target.get_basename())
os.makedirs(self.get_target_private_dir_abs(target), exist_ok=True)
compile_args = swiftc.get_compile_only_args()
compile_args += swiftc.get_module_args(module_name)
link_args = swiftc.get_output_args(os.path.join(self.environment.get_build_dir(), self.get_target_filename(target)))
rundir = self.get_target_private_dir(target)
out_module_name = self.swift_module_file_name(target)
in_module_files = self.determine_swift_dep_modules(target)
abs_module_dirs = self.determine_swift_dep_dirs(target)
module_includes = []
for x in abs_module_dirs:
module_includes += swiftc.get_include_args(x)
link_deps = self.get_swift_link_deps(target)
abs_link_deps = [os.path.join(self.environment.get_build_dir(), x) for x in link_deps]
(rel_generated, _) = self.split_swift_generated_sources(target)
abs_generated = [os.path.join(self.environment.get_build_dir(), x) for x in rel_generated]
# We need absolute paths because swiftc needs to be invoked in a subdir
# and this is the easiest way about it.
objects = [] # Relative to swift invocation dir
rel_objects = [] # Relative to build.ninja
for i in abssrc + abs_generated:
base = os.path.split(i)[1]
oname = os.path.splitext(base)[0] + '.o'
objects.append(oname)
rel_objects.append(os.path.join(self.get_target_private_dir(target), oname))
# Swiftc does not seem to be able to emit objects and module files in one go.
elem = NinjaBuildElement(rel_objects,
'swift_COMPILER',
abssrc)
elem.add_dep(in_module_files + rel_generated)
elem.add_dep(abs_headers)
elem.add_item('ARGS', compile_args + header_imports + abs_generated + module_includes)
elem.add_item('RUNDIR', rundir)
elem.write(outfile)
self.check_outputs(elem)
elem = NinjaBuildElement(out_module_name,
'swift_COMPILER',
abssrc)
elem.add_dep(in_module_files + rel_generated)
elem.add_item('ARGS', compile_args + abs_generated + module_includes + swiftc.get_mod_gen_args())
elem.add_item('RUNDIR', rundir)
elem.write(outfile)
self.check_outputs(elem)
if isinstance(target, build.StaticLibrary):
elem = self.generate_link(target, outfile, self.get_target_filename(target),
rel_objects, self.build.static_linker)
elem.write(outfile)
elif isinstance(target, build.Executable):
elem = NinjaBuildElement(self.get_target_filename(target), 'swift_COMPILER', [])
elem.add_dep(rel_objects)
elem.add_dep(link_deps)
elem.add_item('ARGS', link_args + swiftc.get_std_exe_link_args() + objects + abs_link_deps)
elem.add_item('RUNDIR', rundir)
elem.write(outfile)
self.check_outputs(elem)
else:
raise MesonException('Swift supports only executable and static library targets.')
def generate_static_link_rules(self, is_cross, outfile):
if self.build.has_language('java'):
if not is_cross:
self.generate_java_link(outfile)
if is_cross:
if self.environment.cross_info.need_cross_compiler():
static_linker = self.build.static_cross_linker
else:
static_linker = self.build.static_linker
crstr = '_CROSS'
else:
static_linker = self.build.static_linker
crstr = ''
if static_linker is None:
return
rule = 'rule STATIC%s_LINKER\n' % crstr
if mesonlib.is_windows():
command_templ = ''' command = %s @$out.rsp
rspfile = $out.rsp
rspfile_content = $LINK_ARGS %s $in
'''
else:
command_templ = ' command = %s $LINK_ARGS %s $in\n'
command = command_templ %\
(' '.join(static_linker.get_exelist()),
' '.join(static_linker.get_output_args('$out')))
description = ' description = Static linking library $out\n\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
def generate_dynamic_link_rules(self, outfile):
ctypes = [(self.build.compilers, False)]
if self.environment.is_cross_build():
if self.environment.cross_info.need_cross_compiler():
ctypes.append((self.build.cross_compilers, True))
else:
# Native compiler masquerades as the cross compiler.
ctypes.append((self.build.compilers, True))
else:
ctypes.append((self.build.cross_compilers, True))
for (complist, is_cross) in ctypes:
for compiler in complist:
langname = compiler.get_language()
if langname == 'java' or langname == 'vala' or\
langname == 'rust' or langname == 'cs':
continue
crstr = ''
cross_args = []
if is_cross:
crstr = '_CROSS'
try:
cross_args = self.environment.cross_info.config['properties'][langname + '_link_args']
except KeyError:
pass
rule = 'rule %s%s_LINKER\n' % (langname, crstr)
if mesonlib.is_windows():
command_template = ''' command = %s @$out.rsp
rspfile = $out.rsp
rspfile_content = %s $ARGS %s $in $LINK_ARGS $aliasing
'''
else:
command_template = ' command = %s %s $ARGS %s $in $LINK_ARGS $aliasing\n'
command = command_template % \
(' '.join(compiler.get_linker_exelist()),\
' '.join(cross_args),\
' '.join(compiler.get_linker_output_args('$out')))
description = ' description = Linking target $out'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write('\n')
scriptdir = self.environment.get_script_dir()
outfile.write('\n')
symrule = 'rule SHSYM\n'
symcmd = ' command = "%s" "%s" %s %s $CROSS\n' % (ninja_quote(sys.executable),
ninja_quote(os.path.join(scriptdir, 'symbolextractor.py')),
'$in', '$out')
synstat = ' restat = 1\n'
syndesc = ' description = Generating symbol file $out.\n'
outfile.write(symrule)
outfile.write(symcmd)
outfile.write(synstat)
outfile.write(syndesc)
outfile.write('\n')
def generate_java_compile_rule(self, compiler, outfile):
rule = 'rule %s_COMPILER\n' % compiler.get_language()
invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()])
command = ' command = %s $ARGS $in\n' % invoc
description = ' description = Compiling Java object $in.\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write('\n')
def generate_cs_compile_rule(self, compiler, outfile):
rule = 'rule %s_COMPILER\n' % compiler.get_language()
invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()])
command = ' command = %s $ARGS $in\n' % invoc
description = ' description = Compiling cs target $out.\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write('\n')
def generate_vala_compile_rules(self, compiler, outfile):
rule = 'rule %s_COMPILER\n' % compiler.get_language()
invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()])
command = ' command = %s $ARGS $in\n' % invoc
description = ' description = Compiling Vala source $in.\n'
restat = ' restat = 1\n' # ValaC does this always to take advantage of it.
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write(restat)
outfile.write('\n')
def generate_rust_compile_rules(self, compiler, outfile):
rule = 'rule %s_COMPILER\n' % compiler.get_language()
invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()])
command = ' command = %s $ARGS $in\n' % invoc
description = ' description = Compiling Rust source $in.\n'
depfile = ' depfile = $targetdep\n'
depstyle = ' deps = gcc\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write(depfile)
outfile.write(depstyle)
outfile.write('\n')
def generate_swift_compile_rules(self, compiler, outfile):
rule = 'rule %s_COMPILER\n' % compiler.get_language()
full_exe = [sys.executable,
os.path.join(self.environment.get_script_dir(), 'dirchanger.py'),
'$RUNDIR'] + compiler.get_exelist()
invoc = ' '.join([ninja_quote(i) for i in full_exe])
command = ' command = %s $ARGS $in\n' % invoc
description = ' description = Compiling Swift source $in.\n'
outfile.write(rule)
outfile.write(command)
outfile.write(description)
outfile.write('\n')
def generate_fortran_dep_hack(self, outfile):
if mesonlib.is_windows():
cmd = 'cmd /C ""'
else:
cmd = 'true'
template = '''# Workaround for these issues:
# https://groups.google.com/forum/#!topic/ninja-build/j-2RfBIOd_8
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=47485
rule FORTRAN_DEP_HACK
command = %s
description = Dep hack
restat = 1
'''
outfile.write(template % cmd)
def generate_compile_rule_for(self, langname, compiler, qstr, is_cross, outfile):
if langname == 'java':
if not is_cross:
self.generate_java_compile_rule(compiler, outfile)
return
if langname == 'cs':
if not is_cross:
self.generate_cs_compile_rule(compiler, outfile)
return
if langname == 'vala':
if not is_cross:
self.generate_vala_compile_rules(compiler, outfile)
return
if langname == 'rust':
if not is_cross:
self.generate_rust_compile_rules(compiler, outfile)
return
if langname == 'swift':
if not is_cross:
self.generate_swift_compile_rules(compiler, outfile)
return
if langname == 'fortran':
self.generate_fortran_dep_hack(outfile)
if is_cross:
crstr = '_CROSS'
else:
crstr = ''
rule = 'rule %s%s_COMPILER\n' % (langname, crstr)
depargs = compiler.get_dependency_gen_args('$out', '$DEPFILE')
quoted_depargs = []
for d in depargs:
if d != '$out' and d != '$in':
d = qstr % d
quoted_depargs.append(d)
cross_args = []
if is_cross:
try:
cross_args = self.environment.cross_info.config['properties'][langname + '_args']
except KeyError:
pass
if mesonlib.is_windows():
command_template = ''' command = %s @$out.rsp
rspfile = $out.rsp
rspfile_content = %s $ARGS %s %s %s $in
'''
else:
command_template = ' command = %s %s $ARGS %s %s %s $in\n'
command = command_template % \
(' '.join(compiler.get_exelist()),\
' '.join(cross_args),
' '.join(quoted_depargs),\
' '.join(compiler.get_output_args('$out')),\
' '.join(compiler.get_compile_only_args()))
description = ' description = Compiling %s object $out\n' % langname
if compiler.get_id() == 'msvc':
deps = ' deps = msvc\n'
else:
deps = ' deps = gcc\n'
deps += ' depfile = $DEPFILE\n'
outfile.write(rule)
outfile.write(command)
outfile.write(deps)
outfile.write(description)
outfile.write('\n')
def generate_pch_rule_for(self, langname, compiler, qstr, is_cross, outfile):
if langname != 'c' and langname != 'cpp':
return
if is_cross:
crstr = '_CROSS'
else:
crstr = ''
rule = 'rule %s%s_PCH\n' % (langname, crstr)
depargs = compiler.get_dependency_gen_args('$out', '$DEPFILE')
cross_args = []
if is_cross:
try:
cross_args = self.environment.cross_info.config['properties'][langname + '_args']
except KeyError:
pass
quoted_depargs = []
for d in depargs:
if d != '$out' and d != '$in':
d = qstr % d
quoted_depargs.append(d)
if compiler.get_id() == 'msvc':
output = ''
else:
output = ' '.join(compiler.get_output_args('$out'))
command = " command = %s %s $ARGS %s %s %s $in\n" % \
(' '.join(compiler.get_exelist()),\
' '.join(cross_args),\
' '.join(quoted_depargs),\
output,\
' '.join(compiler.get_compile_only_args()))
description = ' description = Precompiling header %s\n' % '$in'
if compiler.get_id() == 'msvc':
deps = ' deps = msvc\n'
else:
deps = ' deps = gcc\n'
deps += ' depfile = $DEPFILE\n'
outfile.write(rule)
outfile.write(command)
outfile.write(deps)
outfile.write(description)
outfile.write('\n')
def generate_compile_rules(self, outfile):
qstr = quote_char + "%s" + quote_char
for compiler in self.build.compilers:
langname = compiler.get_language()
self.generate_compile_rule_for(langname, compiler, qstr, False, outfile)
self.generate_pch_rule_for(langname, compiler, qstr, False, outfile)
if self.environment.is_cross_build():
# In case we are going a target-only build, make the native compilers
# masquerade as cross compilers.
if self.environment.cross_info.need_cross_compiler():
cclist = self.build.cross_compilers
else:
cclist = self.build.compilers
for compiler in cclist:
langname = compiler.get_language()
self.generate_compile_rule_for(langname, compiler, qstr, True, outfile)
self.generate_pch_rule_for(langname, compiler, qstr, True, outfile)
outfile.write('\n')
def replace_outputs(self, args, private_dir, output_list):
newargs = []
regex = re.compile('@OUTPUT(\d+)@')
for arg in args:
m = regex.search(arg)
while m is not None:
index = int(m.group(1))
src = '@OUTPUT%d@' % index
arg = arg.replace(src, os.path.join(private_dir, output_list[index]))
m = regex.search(arg)
newargs.append(arg)
return newargs
def generate_custom_generator_rules(self, target, outfile):
for genlist in target.get_generated_sources():
if isinstance(genlist, build.CustomTarget):
continue # Customtarget has already written its output rules
generator = genlist.get_generator()
exe = generator.get_exe()
exe_arr = self.exe_object_to_cmd_array(exe)
infilelist = genlist.get_infilelist()
outfilelist = genlist.get_outfilelist()
base_args = generator.get_arglist()
extra_dependencies = [os.path.join(self.build_to_src, i) for i in genlist.extra_depends]
for i in range(len(infilelist)):
if len(generator.outputs) == 1:
sole_output = os.path.join(self.get_target_private_dir(target), outfilelist[i])
else:
sole_output = ''
curfile = infilelist[i]
infilename = os.path.join(self.build_to_src, curfile)
outfiles = genlist.get_outputs_for(curfile)
outfiles = [os.path.join(self.get_target_private_dir(target), of) for of in outfiles]
args = [x.replace("@INPUT@", infilename).replace('@OUTPUT@', sole_output)\
for x in base_args]
args = self.replace_outputs(args, self.get_target_private_dir(target), outfilelist)
# We have consumed output files, so drop them from the list of remaining outputs.
if sole_output == '':
outfilelist = outfilelist[len(generator.outputs):]
relout = self.get_target_private_dir(target)
args = [x.replace("@SOURCE_DIR@", self.build_to_src).replace("@BUILD_DIR@", relout)
for x in args]
final_args = []
for a in args:
if a == '@EXTRA_ARGS@':
final_args += genlist.get_extra_args()
else:
final_args.append(a)
cmdlist = exe_arr + final_args
elem = NinjaBuildElement(outfiles, 'CUSTOM_COMMAND', infilename)
if len(extra_dependencies) > 0:
elem.add_dep(extra_dependencies)
elem.add_item('DESC', 'Generating $out')
if isinstance(exe, build.BuildTarget):
elem.add_dep(self.get_target_filename(exe))
elem.add_item('COMMAND', cmdlist)
elem.write(outfile)
self.check_outputs(elem)
def scan_fortran_module_outputs(self, target):
compiler = None
for c in self.build.compilers:
if c.get_language() == 'fortran':
compiler = c
break
if compiler is None:
self.fortran_deps[target.get_basename()] = {}
return
modre = re.compile(r"\s*module\s+(\w+)", re.IGNORECASE)
module_files = {}
for s in target.get_sources():
# FIXME, does not work for generated Fortran sources,
# but those are really rare. I hope.
if not compiler.can_compile(s):
continue
for line in open(os.path.join(self.environment.get_source_dir(), s.subdir, s.fname)):
modmatch = modre.match(line)
if modmatch is not None:
modname = modmatch.group(1)
if modname.lower() == 'procedure': # MODULE PROCEDURE construct
continue
if modname in module_files:
raise InvalidArguments('Namespace collision: module %s defined in two files %s and %s.' %
(modname, module_files[modname], s))
module_files[modname] = s
self.fortran_deps[target.get_basename()] = module_files
def get_fortran_deps(self, compiler, src, target):
mod_files = []
usere = re.compile(r"\s*use\s+(\w+)", re.IGNORECASE)
dirname = self.get_target_private_dir(target)
tdeps= self.fortran_deps[target.get_basename()]
for line in open(src):
usematch = usere.match(line)
if usematch is not None:
usename = usematch.group(1)
if usename not in tdeps:
# The module is not provided by any source file. This is due to
# a) missing file/typo/etc
# b) using a module provided by the compiler, such as OpenMP
# There's no easy way to tell which is which (that I know of)
# so just ignore this and go on. Ideally we would print a
# warning message to the user but this is a common occurrance,
# which would lead to lots of distracting noise.
continue
mod_source_file = tdeps[usename]
# Check if a source uses a module it exports itself.
# Potential bug if multiple targets have a file with
# the same name.
if mod_source_file.fname == os.path.split(src)[1]:
continue
mod_name = compiler.module_name_to_filename(usematch.group(1))
mod_files.append(os.path.join(dirname, mod_name))
return mod_files
def generate_single_compile(self, target, outfile, src, is_generated=False, header_deps=[], order_deps=[]):
if(isinstance(src, str) and src.endswith('.h')):
raise RuntimeError('Fug')
if isinstance(src, RawFilename) and src.fname.endswith('.h'):
raise RuntimeError('Fug')
extra_orderdeps = []
compiler = self.get_compiler_for_source(src)
commands = self.generate_basic_compiler_args(target, compiler)
commands += compiler.get_include_args(self.get_target_private_dir(target), False)
curdir = target.get_subdir()
tmppath = os.path.normpath(os.path.join(self.build_to_src, curdir))
commands += compiler.get_include_args(tmppath, False)
if curdir == '':
curdir = '.'
commands += compiler.get_include_args(curdir, False)
for d in target.external_deps:
if d.need_threads():
commands += compiler.thread_flags()
break
if isinstance(src, RawFilename):
rel_src = src.fname
elif is_generated:
if self.has_dir_part(src):
rel_src = src
else:
rel_src = os.path.join(self.get_target_private_dir(target), src)
abs_src = os.path.join(self.environment.get_source_dir(), rel_src)
else:
if isinstance(src, File):
rel_src = src.rel_to_builddir(self.build_to_src)
else:
raise build.InvalidArguments('Invalid source type.')
abs_src = os.path.join(self.environment.get_build_dir(), rel_src)
if isinstance(src, RawFilename):
src_filename = src.fname
elif isinstance(src, File):
src_filename = src.fname
elif os.path.isabs(src):
src_filename = os.path.basename(src)
else:
src_filename = src
obj_basename = src_filename.replace('/', '_').replace('\\', '_')
rel_obj = os.path.join(self.get_target_private_dir(target), obj_basename)
rel_obj += '.' + self.environment.get_object_suffix()
dep_file = compiler.depfile_for_object(rel_obj)
if self.environment.coredata.get_builtin_option('use_pch'):
pchlist = target.get_pch(compiler.language)
else:
pchlist = []
if len(pchlist) == 0:
pch_dep = []
else:
arr = []
i = os.path.join(self.get_target_private_dir(target), compiler.get_pch_name(pchlist[0]))
arr.append(i)
pch_dep = arr
for i in target.get_include_dirs():
basedir = i.get_curdir()
for d in i.get_incdirs():
expdir = os.path.join(basedir, d)
srctreedir = os.path.join(self.build_to_src, expdir)
bargs = compiler.get_include_args(expdir, i.is_system)
sargs = compiler.get_include_args(srctreedir, i.is_system)
commands += bargs
commands += sargs
for d in i.get_extra_build_dirs():
commands += compiler.get_include_args(d, i.is_system)
custom_target_include_dirs = []
for i in target.generated:
if isinstance(i, build.CustomTarget):
idir = self.get_target_dir(i)
if idir not in custom_target_include_dirs:
custom_target_include_dirs.append(idir)
for i in custom_target_include_dirs:
commands+= compiler.get_include_args(i, False)
if self.environment.coredata.get_builtin_option('use_pch'):
commands += self.get_pch_include_args(compiler, target)
crstr = ''
if target.is_cross:
crstr = '_CROSS'
compiler_name = '%s%s_COMPILER' % (compiler.get_language(), crstr)
extra_deps = []
if compiler.get_language() == 'fortran':
extra_deps += self.get_fortran_deps(compiler, abs_src, target)
# Dependency hack. Remove once multiple outputs in Ninja is fixed:
# https://groups.google.com/forum/#!topic/ninja-build/j-2RfBIOd_8
for modname, srcfile in self.fortran_deps[target.get_basename()].items():
modfile = os.path.join(self.get_target_private_dir(target),
compiler.module_name_to_filename(modname))
if srcfile == src:
depelem = NinjaBuildElement(modfile, 'FORTRAN_DEP_HACK', rel_obj)
depelem.write(outfile)
self.check_outputs(depelem)
commands += compiler.get_module_outdir_args(self.get_target_private_dir(target))
element = NinjaBuildElement(rel_obj, compiler_name, rel_src)
for d in header_deps:
if isinstance(d, RawFilename):
d = d.fname
elif not self.has_dir_part(d):
d = os.path.join(self.get_target_private_dir(target), d)
element.add_dep(d)
for d in extra_deps:
element.add_dep(d)
for d in order_deps:
if isinstance(d, RawFilename):
d = d.fname
elif not self.has_dir_part(d):
d = os.path.join(self.get_target_private_dir(target), d)
element.add_orderdep(d)
element.add_orderdep(pch_dep)
element.add_orderdep(extra_orderdeps)
for i in self.get_fortran_orderdeps(target, compiler):
element.add_orderdep(i)
element.add_item('DEPFILE', dep_file)
element.add_item('ARGS', commands)
element.write(outfile)
self.check_outputs(element)
return rel_obj
def has_dir_part(self, fname):
return '/' in fname or '\\' in fname
# Fortran is a bit weird (again). When you link against a library, just compiling a source file
# requires the mod files that are output when single files are built. To do this right we would need to
# scan all inputs and write out explicit deps for each file. That is stoo slow and too much effort so
# instead just have an ordered dependendy on the library. This ensures all required mod files are created.
# The real deps are then detected via dep file generation from the compiler. This breaks on compilers that
# produce incorrect dep files but such is life.
def get_fortran_orderdeps(self, target, compiler):
if compiler.language != 'fortran':
return []
return [os.path.join(self.get_target_dir(lt), lt.get_filename()) for lt in target.link_targets]
def generate_msvc_pch_command(self, target, compiler, pch):
if len(pch) != 2:
raise RuntimeError('MSVC requires one header and one source to produce precompiled headers.')
header = pch[0]
source = pch[1]
pchname = compiler.get_pch_name(header)
dst = os.path.join(self.get_target_private_dir(target), pchname)
commands = []
commands += self.generate_basic_compiler_args(target, compiler)
just_name = os.path.split(header)[1]
(objname, pch_args) = compiler.gen_pch_args(just_name, source, dst)
commands += pch_args
dep = dst + '.' + compiler.get_depfile_suffix()
return (commands, dep, dst, [objname])
def generate_gcc_pch_command(self, target, compiler, pch):
commands = []
commands += self.generate_basic_compiler_args(target, compiler)
dst = os.path.join(self.get_target_private_dir(target),
os.path.split(pch)[-1] + '.' + compiler.get_pch_suffix())
dep = dst + '.' + compiler.get_depfile_suffix()
return (commands, dep, dst, []) # Gcc does not create an object file during pch generation.
def generate_pch(self, target, outfile):
cstr = ''
pch_objects = []
if target.is_cross:
cstr = '_CROSS'
for lang in ['c', 'cpp']:
pch = target.get_pch(lang)
if len(pch) == 0:
continue
if '/' not in pch[0] or '/' not in pch[-1]:
raise build.InvalidArguments('Precompiled header of "%s" must not be in the same directory as source, please put it in a subdirectory.' % target.get_basename())
compiler = self.get_compiler_for_lang(lang)
if compiler.id == 'msvc':
src = os.path.join(self.build_to_src, target.get_source_subdir(), pch[-1])
(commands, dep, dst, objs) = self.generate_msvc_pch_command(target, compiler, pch)
extradep = os.path.join(self.build_to_src, target.get_source_subdir(), pch[0])
else:
src = os.path.join(self.build_to_src, target.get_source_subdir(), pch[0])
(commands, dep, dst, objs) = self.generate_gcc_pch_command(target, compiler, pch[0])
extradep = None
pch_objects += objs
rulename = compiler.get_language() + cstr + '_PCH'
elem = NinjaBuildElement(dst, rulename, src)
if extradep is not None:
elem.add_dep(extradep)
elem.add_item('ARGS', commands)
elem.add_item('DEPFILE', dep)
elem.write(outfile)
self.check_outputs(elem)
return pch_objects
def generate_shsym(self, outfile, target):
target_name = self.get_target_filename(target)
targetdir = self.get_target_private_dir(target)
symname = os.path.join(targetdir, target_name + '.symbols')
elem = NinjaBuildElement(symname, 'SHSYM', target_name)
if self.environment.is_cross_build() and self.environment.cross_info.need_cross_compiler():
elem.add_item('CROSS', '--cross-host=' + self.environment.cross_info.config['host_machine']['system'])
elem.write(outfile)
self.check_outputs(elem)
def generate_link(self, target, outfile, outname, obj_list, linker, extra_args=[]):
if isinstance(target, build.StaticLibrary):
linker_base = 'STATIC'
else:
linker_base = linker.get_language() # Fixme.
if isinstance(target, build.SharedLibrary):
self.generate_shsym(outfile, target)
crstr = ''
if target.is_cross:
crstr = '_CROSS'
linker_rule = linker_base + crstr + '_LINKER'
abspath = os.path.join(self.environment.get_build_dir(), target.subdir)
commands = []
commands += linker.get_linker_always_args()
commands += linker.get_buildtype_linker_args(self.environment.coredata.get_builtin_option('buildtype'))
commands += linker.get_option_link_args(self.environment.coredata.compiler_options)
if not(isinstance(target, build.StaticLibrary)):
commands += self.environment.coredata.external_link_args[linker.get_language()]
if isinstance(target, build.Executable):
commands += linker.get_std_exe_link_args()
elif isinstance(target, build.SharedLibrary):
commands += linker.get_std_shared_lib_link_args()
commands += linker.get_pic_args()
if hasattr(target, 'soversion'):
soversion = target.soversion
else:
soversion = None
commands += linker.get_soname_args(target.name, abspath, soversion)
elif isinstance(target, build.StaticLibrary):
commands += linker.get_std_link_args()
else:
raise RuntimeError('Unknown build target type.')
# Link arguments of static libraries are not put in the command line of
# the library. They are instead appended to the command line where
# the static library is used.
if linker_base == 'STATIC':
dependencies = []
else:
dependencies = target.get_dependencies()
commands += self.build_target_link_arguments(linker, dependencies)
for d in target.external_deps:
if d.need_threads():
commands += linker.thread_link_flags()
if not isinstance(target, build.StaticLibrary):
commands += target.link_args
# External deps must be last because target link libraries may depend on them.
if not(isinstance(target, build.StaticLibrary)):
for dep in target.get_external_deps():
commands += dep.get_link_args()
for d in target.get_dependencies():
if isinstance(d, build.StaticLibrary):
for dep in d.get_external_deps():
commands += dep.get_link_args()
commands += linker.build_rpath_args(self.environment.get_build_dir(),\
self.determine_rpath_dirs(target), target.install_rpath)
if self.environment.coredata.get_builtin_option('coverage'):
commands += linker.get_coverage_link_args()
custom_target_libraries = self.get_custom_target_provided_libraries(target)
commands += extra_args
commands += custom_target_libraries
commands = linker.unixtype_flags_to_native(commands)
dep_targets = [self.get_dependency_filename(t) for t in dependencies]
dep_targets += [os.path.join(self.environment.source_dir,
target.subdir, t) for t in target.link_depends]
elem = NinjaBuildElement(outname, linker_rule, obj_list)
elem.add_dep(dep_targets + custom_target_libraries)
elem.add_item('LINK_ARGS', commands)
self.check_outputs(elem)
return elem
def get_custom_target_provided_libraries(self, target):
libs = []
for t in target.get_generated_sources():
if not isinstance(t, build.CustomTarget):
continue
for f in t.output:
if self.environment.is_library(f):
libs.append(os.path.join(self.get_target_dir(t), f))
return libs
def determine_rpath_dirs(self, target):
link_deps = target.get_all_link_deps()
result = []
for ld in link_deps:
prospective = self.get_target_dir(ld)
if not prospective in result:
result.append(prospective)
return result
def get_dependency_filename(self, t):
if isinstance(t, build.SharedLibrary):
return os.path.join(self.get_target_private_dir(t), self.get_target_filename(t) + '.symbols')
return self.get_target_filename(t)
def generate_shlib_aliases(self, target, outdir):
basename = target.get_filename()
aliases = target.get_aliaslist()
if not mesonlib.is_windows():
for alias in aliases:
aliasfile = os.path.join(self.environment.get_build_dir(), outdir, alias)
try:
os.remove(aliasfile)
except Exception:
pass
os.symlink(basename, aliasfile)
else:
mlog.debug("Library versioning disabled because host does not support symlinks.")
def generate_gcov_clean(self, outfile):
gcno_elem = NinjaBuildElement('clean-gcno', 'CUSTOM_COMMAND', 'PHONY')
script_root = self.environment.get_script_dir()
clean_script = os.path.join(script_root, 'delwithsuffix.py')
gcno_elem.add_item('COMMAND', [sys.executable, clean_script, '.', 'gcno'])
gcno_elem.add_item('description', 'Deleting gcno files')
gcno_elem.write(outfile)
self.check_outputs(gcno_elem)
gcda_elem = NinjaBuildElement('clean-gcda', 'CUSTOM_COMMAND', 'PHONY')
script_root = self.environment.get_script_dir()
clean_script = os.path.join(script_root, 'delwithsuffix.py')
gcda_elem.add_item('COMMAND', [sys.executable, clean_script, '.', 'gcda'])
gcda_elem.add_item('description', 'Deleting gcda files')
gcda_elem.write(outfile)
self.check_outputs(gcda_elem)
def is_compilable_file(self, filename):
if filename.endswith('.cpp') or\
filename.endswith('.c') or\
filename.endswith('.cxx') or\
filename.endswith('.cc') or\
filename.endswith('.C'):
return True
return False
def process_dep_gens(self, outfile, target):
src_deps = []
other_deps = []
for rule in self.dep_rules.values():
srcs = target.get_original_kwargs().get(rule.src_keyword, [])
if isinstance(srcs, str):
srcs = [srcs]
for src in srcs:
plainname = os.path.split(src)[1]
basename = plainname.split('.')[0]
outname = rule.name_templ.replace('@BASENAME@', basename).replace('@PLAINNAME@', plainname)
outfilename = os.path.join(self.get_target_private_dir(target), outname)
infilename = os.path.join(self.build_to_src, target.get_source_subdir(), src)
elem = NinjaBuildElement(outfilename, rule.name, infilename)
elem.write(outfile)
self.check_outputs(elem)
if self.is_compilable_file(outfilename):
src_deps.append(outfilename)
else:
other_deps.append(outfilename)
return (src_deps, other_deps)
def generate_ending(self, outfile):
targetlist = [self.get_target_filename(t) for t in self.build.get_targets().values()\
if not isinstance(t, build.RunTarget)]
elem = NinjaBuildElement('all', 'phony', targetlist)
elem.write(outfile)
self.check_outputs(elem)
default = 'default all\n\n'
outfile.write(default)
ninja_command = environment.detect_ninja()
if ninja_command is None:
raise MesonException('Could not detect ninja command')
elem = NinjaBuildElement('clean', 'CUSTOM_COMMAND', 'PHONY')
elem.add_item('COMMAND', [ninja_command, '-t', 'clean'])
elem.add_item('description', 'Cleaning')
if self.environment.coredata.get_builtin_option('coverage'):
self.generate_gcov_clean(outfile)
elem.add_dep('clean-gcda')
elem.add_dep('clean-gcno')
elem.write(outfile)
self.check_outputs(elem)
deps = self.get_regen_filelist()
elem = NinjaBuildElement('build.ninja', 'REGENERATE_BUILD', deps)
elem.add_item('pool', 'console')
elem.write(outfile)
elem = NinjaBuildElement(deps, 'phony', '')
elem.write(outfile)
self.check_outputs(elem)
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y-tetsu/othello | tests/strategies/common/test_cputime.py | 73eabfe22d6b44bbfa0b436e6287e3e7356620f4 | """Tests of cputime.py
"""
import unittest
from reversi.strategies.common import CPU_TIME
class TestCputime(unittest.TestCase):
"""cputime
"""
def test_cputime(self):
self.assertEqual(CPU_TIME, 0.5)
| [] |
coopersigrist/RecurrentNeuralSystem- | experiments/cifar10_recon.py | bd5bb680ec7f2166547709195f7bb3cd52cca5e8 | # -*- coding: utf-8 -*-
"""ReNS experiments - CIFAR10
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1byZ4xTfCK2x1Rhkxpl-Vv4sqA-bo4bis
# SETUP
"""
#@title Insatlling Pyorch
# !pip install torch
# !pip install torchvision
#@title Import Dependencies
import numpy as np
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
from tqdm import tqdm
from typing import Optional, Union, Tuple, List, Sequence, Iterable
import math
from scipy.spatial.distance import euclidean
from torch.nn.modules.utils import _pair
from torchvision import models
from sklearn.metrics import jaccard_score
import matplotlib.pyplot as plt
from models.models import RegularAutoEncoder, ModulatedAutoEncoder, PseudoRecAutoEncoder
"""# TRAINING"""
batch_size = 32
num_epochs = 5
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# Load MNIST data.
train_data = dsets.CIFAR10(root = './data', train = True,
transform = transform, download = True)
test_data = dsets.CIFAR10(root = './data', train = False,
transform = transform)
train_gen = torch.utils.data.DataLoader(dataset = train_data,
batch_size = batch_size,
shuffle = True)
test_gen = torch.utils.data.DataLoader(dataset = test_data,
batch_size = batch_size,
shuffle = False)
reflexor_size = 500
image_size = 32
channels = 3
# net = recurrentLayer(784, 784, 10, 5, 10, 0)
net1 = RegularAutoEncoder(channels * image_size ** 2, channels * image_size ** 2, reflexor_size)
net2 = ModulatedAutoEncoder(channels * image_size ** 2, channels * image_size ** 2, reflexor_size)
net3 = PseudoRecAutoEncoder(channels * image_size ** 2, channels * image_size ** 2, reflexor_size)
lr = .0001 # size of step
loss_function = nn.MSELoss()
# Unnormalize the image to display it
def img_fix(img):
return np.transpose((img / 2 + 0.5).numpy(), (1, 2, 0))
# Commented out IPython magic to ensure Python compatibility.
train_losses = [[],[],[]]
test_losses = [[],[],[]]
real_imgs = [[],[],[]]
reconstructed_imgs = [[],[],[]]
param_counts = np.ones(3)
steps = [[],[],[]]
for num, net in enumerate([net1, net2, net3]):
optimizer = torch.optim.Adam( net.parameters(), lr=lr)
param_counts[num] = (sum(p.numel() for p in net.parameters() if p.requires_grad))
for epoch in range(num_epochs):
for i ,(images,labels) in enumerate(train_gen):
#images = Variable(images.view(-1,28*28))
labels = Variable(images.view(-1,3 * image_size ** 2))
optimizer.zero_grad()
outputs = net(images)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 300 == 0:
temp_loss = loss.item()
print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f'
%(epoch+1, num_epochs, i+1, len(train_data)//batch_size, temp_loss))
dupe = Variable(outputs[0].data, requires_grad=False)
# plt.imshow(img_fix(images[0]))
# plt.show()
# plt.imshow(img_fix(dupe.view(3, image_size, image_size)))
# plt.show()
train_losses[num].append(temp_loss)
steps[num].append((50000 * epoch) + ((i + 1) * batch_size))
real_imgs[num].append(img_fix(images[0]))
reconstructed_imgs[num].append(img_fix(dupe.view(3, image_size, image_size)))
# Test Data
score = 0
total = 0
for images,labels in test_gen:
#images = Variable(images.view(-1,784))
output = net(images)
score += loss_function(output, images.view(-1, 3 * image_size ** 2)).item()
test_losses[num].append((score))
plt.plot(steps[0], train_losses[0], label= "Baseline")
plt.plot(steps[1], train_losses[1], label= "Modulated")
plt.plot(steps[2], train_losses[2], label= "Recurrent with Modulation")
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.title('Training loss history')
plt.legend()
plt.show()
plt.plot(steps[0], test_losses[0], label= "Baseline")
plt.plot(steps[1], test_losses[1], label= "Modulated")
plt.plot(steps[2], test_losses[2], label= "Recurrent with Modulation")
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.title('Testing loss history')
plt.legend()
plt.show()
for num,count in enumerate(param_counts):
param_counts[num] /= 1000
plt.bar(["Base", "Modulated", "ReNS"], param_counts)
plt.xlabel('Model')
plt.ylabel('# of thousands of Parameters')
plt.show()
from mpl_toolkits.axes_grid1 import ImageGrid
num_smaples = len(real_imgs[0])
for num in [0,1,2]:
fig = plt.figure(figsize=(20.,20.))
grid = ImageGrid(fig, 111, # similar to subplot(111)
nrows_ncols=(2, num_smaples), # creates 2x2 grid of axes
axes_pad=0.1, # pad between axes in inch.
)
for ax, im in zip(grid, real_imgs[num]+reconstructed_imgs[num]):
# Iterating over the grid returns the Axes.
ax.imshow(im)
ax.axis("off")
plt.show()
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ameoba/horizon | horizon/forms/__init__.py | ff9e367c98a8bb79f10914abffaaa04b0a461819 | # vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2012 Nebula, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
# FIXME(gabriel): Legacy imports for API compatibility.
from django.forms import * # noqa
from django.forms import widgets
# Convenience imports for public API components.
from horizon.forms.base import DateForm # noqa
from horizon.forms.base import SelfHandlingForm # noqa
from horizon.forms.base import SelfHandlingMixin # noqa
from horizon.forms.fields import DynamicChoiceField # noqa
from horizon.forms.fields import DynamicTypedChoiceField # noqa
from horizon.forms.views import ModalFormMixin # noqa
from horizon.forms.views import ModalFormView # noqa
assert widgets
assert SelfHandlingMixin
assert SelfHandlingForm
assert DateForm
assert ModalFormView
assert ModalFormMixin
assert DynamicTypedChoiceField
assert DynamicChoiceField
| [] |
noironetworks/heat | heat/tests/test_rpc_listener_client.py | 7cdadf1155f4d94cf8f967635b98e4012a7acfb7 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import mock
import oslo_messaging as messaging
from heat.rpc import api as rpc_api
from heat.rpc import listener_client as rpc_client
from heat.tests import common
class ListenerClientTest(common.HeatTestCase):
@mock.patch('heat.common.messaging.get_rpc_client',
return_value=mock.Mock())
def test_engine_alive_ok(self, rpc_client_method):
mock_rpc_client = rpc_client_method.return_value
mock_prepare_method = mock_rpc_client.prepare
mock_prepare_client = mock_prepare_method.return_value
mock_cnxt = mock.Mock()
listener_client = rpc_client.EngineListenerClient('engine-007')
rpc_client_method.assert_called_once_with(
version=rpc_client.EngineListenerClient.BASE_RPC_API_VERSION,
topic=rpc_api.LISTENER_TOPIC, server='engine-007',
)
mock_prepare_method.assert_called_once_with(timeout=2)
self.assertEqual(mock_prepare_client,
listener_client._client,
"Failed to create RPC client")
ret = listener_client.is_alive(mock_cnxt)
self.assertTrue(ret)
mock_prepare_client.call.assert_called_once_with(mock_cnxt,
'listening')
@mock.patch('heat.common.messaging.get_rpc_client',
return_value=mock.Mock())
def test_engine_alive_timeout(self, rpc_client_method):
mock_rpc_client = rpc_client_method.return_value
mock_prepare_method = mock_rpc_client.prepare
mock_prepare_client = mock_prepare_method.return_value
mock_cnxt = mock.Mock()
listener_client = rpc_client.EngineListenerClient('engine-007')
rpc_client_method.assert_called_once_with(
version=rpc_client.EngineListenerClient.BASE_RPC_API_VERSION,
topic=rpc_api.LISTENER_TOPIC, server='engine-007',
)
mock_prepare_method.assert_called_once_with(timeout=2)
self.assertEqual(mock_prepare_client,
listener_client._client,
"Failed to create RPC client")
mock_prepare_client.call.side_effect = messaging.MessagingTimeout(
'too slow')
ret = listener_client.is_alive(mock_cnxt)
self.assertFalse(ret)
mock_prepare_client.call.assert_called_once_with(mock_cnxt,
'listening')
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akshitsingla/amadeus-python | amadeus/travel/trip_parser_jobs/_status.py | d8f3595e556b674998156f98d8a318045bb4c21c | from amadeus.client.decorator import Decorator
class TripParserStatus(Decorator, object):
def __init__(self, client, job_id):
Decorator.__init__(self, client)
self.job_id = job_id
def get(self, **params):
'''
Returns the parsing status and the link to the result
in case of successful parsing.
.. code-block:: python
amadeus.travel.trip_parser_jobs.status('XXX').get
:rtype: amadeus.Response
:raises amadeus.ResponseError: if the request could not be completed
'''
return self.client.get(
'/v2/travel/trip-parser-jobs/{0}'.format(self.job_id),
**params)
| [((6, 8, 6, 40), 'amadeus.client.decorator.Decorator.__init__', 'Decorator.__init__', ({(6, 27, 6, 31): 'self', (6, 33, 6, 39): 'client'}, {}), '(self, client)', False, 'from amadeus.client.decorator import Decorator\n')] |
meyerweb/wpt | tools/third_party/iniconfig/testing/test_iniconfig.py | f04261533819893c71289614c03434c06856c13e | import py
import pytest
from iniconfig import IniConfig, ParseError, __all__ as ALL
from iniconfig import iscommentline
from textwrap import dedent
check_tokens = {
'section': (
'[section]',
[(0, 'section', None, None)]
),
'value': (
'value = 1',
[(0, None, 'value', '1')]
),
'value in section': (
'[section]\nvalue=1',
[(0, 'section', None, None), (1, 'section', 'value', '1')]
),
'value with continuation': (
'names =\n Alice\n Bob',
[(0, None, 'names', 'Alice\nBob')]
),
'value with aligned continuation': (
'names = Alice\n'
' Bob',
[(0, None, 'names', 'Alice\nBob')]
),
'blank line': (
'[section]\n\nvalue=1',
[(0, 'section', None, None), (2, 'section', 'value', '1')]
),
'comment': (
'# comment',
[]
),
'comment on value': (
'value = 1',
[(0, None, 'value', '1')]
),
'comment on section': (
'[section] #comment',
[(0, 'section', None, None)]
),
'comment2': (
'; comment',
[]
),
'comment2 on section': (
'[section] ;comment',
[(0, 'section', None, None)]
),
'pseudo section syntax in value': (
'name = value []',
[(0, None, 'name', 'value []')]
),
'assignment in value': (
'value = x = 3',
[(0, None, 'value', 'x = 3')]
),
'use of colon for name-values': (
'name: y',
[(0, None, 'name', 'y')]
),
'use of colon without space': (
'value:y=5',
[(0, None, 'value', 'y=5')]
),
'equality gets precedence': (
'value=xyz:5',
[(0, None, 'value', 'xyz:5')]
),
}
@pytest.fixture(params=sorted(check_tokens))
def input_expected(request):
return check_tokens[request.param]
@pytest.fixture
def input(input_expected):
return input_expected[0]
@pytest.fixture
def expected(input_expected):
return input_expected[1]
def parse(input):
# only for testing purposes - _parse() does not use state except path
ini = object.__new__(IniConfig)
ini.path = "sample"
return ini._parse(input.splitlines(True))
def parse_a_error(input):
return py.test.raises(ParseError, parse, input)
def test_tokenize(input, expected):
parsed = parse(input)
assert parsed == expected
def test_parse_empty():
parsed = parse("")
assert not parsed
ini = IniConfig("sample", "")
assert not ini.sections
def test_ParseError():
e = ParseError("filename", 0, "hello")
assert str(e) == "filename:1: hello"
def test_continuation_needs_perceeding_token():
excinfo = parse_a_error(' Foo')
assert excinfo.value.lineno == 0
def test_continuation_cant_be_after_section():
excinfo = parse_a_error('[section]\n Foo')
assert excinfo.value.lineno == 1
def test_section_cant_be_empty():
excinfo = parse_a_error('[]')
assert excinfo.value.lineno == 0
@py.test.mark.parametrize('line', [
'!!',
])
def test_error_on_weird_lines(line):
parse_a_error(line)
def test_iniconfig_from_file(tmpdir):
path = tmpdir/'test.txt'
path.write('[metadata]\nname=1')
config = IniConfig(path=path)
assert list(config.sections) == ['metadata']
config = IniConfig(path, "[diff]")
assert list(config.sections) == ['diff']
with pytest.raises(TypeError):
IniConfig(data=path.read())
def test_iniconfig_section_first(tmpdir):
with pytest.raises(ParseError) as excinfo:
IniConfig("x", data='name=1')
assert excinfo.value.msg == "no section header defined"
def test_iniconig_section_duplicate_fails():
with pytest.raises(ParseError) as excinfo:
IniConfig("x", data='[section]\n[section]')
assert 'duplicate section' in str(excinfo.value)
def test_iniconfig_duplicate_key_fails():
with pytest.raises(ParseError) as excinfo:
IniConfig("x", data='[section]\nname = Alice\nname = bob')
assert 'duplicate name' in str(excinfo.value)
def test_iniconfig_lineof():
config = IniConfig("x.ini", data=(
'[section]\n'
'value = 1\n'
'[section2]\n'
'# comment\n'
'value =2'
))
assert config.lineof('missing') is None
assert config.lineof('section') == 1
assert config.lineof('section2') == 3
assert config.lineof('section', 'value') == 2
assert config.lineof('section2', 'value') == 5
assert config['section'].lineof('value') == 2
assert config['section2'].lineof('value') == 5
def test_iniconfig_get_convert():
config = IniConfig("x", data='[section]\nint = 1\nfloat = 1.1')
assert config.get('section', 'int') == '1'
assert config.get('section', 'int', convert=int) == 1
def test_iniconfig_get_missing():
config = IniConfig("x", data='[section]\nint = 1\nfloat = 1.1')
assert config.get('section', 'missing', default=1) == 1
assert config.get('section', 'missing') is None
def test_section_get():
config = IniConfig("x", data='[section]\nvalue=1')
section = config['section']
assert section.get('value', convert=int) == 1
assert section.get('value', 1) == "1"
assert section.get('missing', 2) == 2
def test_missing_section():
config = IniConfig("x", data='[section]\nvalue=1')
with pytest.raises(KeyError):
config["other"]
def test_section_getitem():
config = IniConfig("x", data='[section]\nvalue=1')
assert config['section']['value'] == '1'
assert config['section']['value'] == '1'
def test_section_iter():
config = IniConfig("x", data='[section]\nvalue=1')
names = list(config['section'])
assert names == ['value']
items = list(config['section'].items())
assert items == [('value', '1')]
def test_config_iter():
config = IniConfig("x.ini", data=dedent('''
[section1]
value=1
[section2]
value=2
'''))
l = list(config)
assert len(l) == 2
assert l[0].name == 'section1'
assert l[0]['value'] == '1'
assert l[1].name == 'section2'
assert l[1]['value'] == '2'
def test_config_contains():
config = IniConfig("x.ini", data=dedent('''
[section1]
value=1
[section2]
value=2
'''))
assert 'xyz' not in config
assert 'section1' in config
assert 'section2' in config
def test_iter_file_order():
config = IniConfig("x.ini", data="""
[section2] #cpython dict ordered before section
value = 1
value2 = 2 # dict ordered before value
[section]
a = 1
b = 2
""")
l = list(config)
secnames = [x.name for x in l]
assert secnames == ['section2', 'section']
assert list(config['section2']) == ['value', 'value2']
assert list(config['section']) == ['a', 'b']
def test_example_pypirc():
config = IniConfig("pypirc", data=dedent('''
[distutils]
index-servers =
pypi
other
[pypi]
repository: <repository-url>
username: <username>
password: <password>
[other]
repository: http://example.com/pypi
username: <username>
password: <password>
'''))
distutils, pypi, other = list(config)
assert distutils["index-servers"] == "pypi\nother"
assert pypi['repository'] == '<repository-url>'
assert pypi['username'] == '<username>'
assert pypi['password'] == '<password>'
assert ['repository', 'username', 'password'] == list(other)
def test_api_import():
assert ALL == ['IniConfig', 'ParseError']
@pytest.mark.parametrize("line", [
"#qwe",
" #qwe",
";qwe",
" ;qwe",
])
def test_iscommentline_true(line):
assert iscommentline(line)
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natebragg/java-sketch | jskparser/jskparser/util.py | f5ac26f2cc46ae4556f9a61c55afd37f55c961ff | import os
from subprocess import call
from . import glob2
pwd = os.path.dirname(__file__)
def get_files_from_path(path, ext):
# use set to remove duplicate files. weird...but it happens
if os.path.isfile(path): return set([os.path.abspath(path)])
else: # i.e., folder
files = glob2.glob(os.path.abspath(os.path.join(path, "**/*.{}".format(ext))))
return set(sorted(files)) # to guarantee the order of files read
"""
handling javajskparser AST
"""
def toAST(files, ext, add_libs):
prg_files = []
for f in files:
prg_files.extend(get_files_from_path(f, "java"))
if not prg_files: exit('jskparser.util: File(s) not found!')
java_in = os.path.abspath(os.path.join(pwd, '../tests/ir_asts/API.java'))
json_out = os.path.abspath(os.path.join(pwd, '../tests/ir_asts/java.json'))
if add_libs:
obj_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Object.java'))
str_path = os.path.abspath(os.path.join(pwd, '../../model/lang/String.java'))
num_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Number.java'))
int_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Integer.java'))
char_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Character.java'))
itbl_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Iterable.java'))
iter_path = os.path.abspath(os.path.join(pwd, '../../model/util/Iterator.java'))
arr_path = os.path.abspath(os.path.join(pwd, '../../model/util/Arrays.java'))
list_path = os.path.abspath(os.path.join(pwd, '../../model/util/List.java'))
alist_path = os.path.abspath(os.path.join(pwd, '../../model/util/ArrayList.java'))
llist_path = os.path.abspath(os.path.join(pwd, '../../model/util/LinkedList.java'))
hmap_path = os.path.abspath(os.path.join(pwd, '../../model/util/HashMap.java'))
hset_path = os.path.abspath(os.path.join(pwd, '../../model/util/HashSet.java'))
if obj_path not in prg_files: prg_files.append(obj_path)
if str_path not in prg_files: prg_files.append(str_path)
if num_path not in prg_files: prg_files.append(num_path)
if int_path not in prg_files: prg_files.append(int_path)
if char_path not in prg_files: prg_files.append(char_path)
if itbl_path not in prg_files: prg_files.append(itbl_path)
if iter_path not in prg_files: prg_files.append(iter_path)
if arr_path not in prg_files: prg_files.append(arr_path)
if list_path not in prg_files: prg_files.append(list_path)
if alist_path not in prg_files: prg_files.append(alist_path)
if llist_path not in prg_files: prg_files.append(llist_path)
if hmap_path not in prg_files: prg_files.append(hmap_path)
if hset_path not in prg_files: prg_files.append(hset_path)
api = ""
for fname in prg_files:
with open(fname, 'r') as fd:
api += fd.read()
with open(java_in, 'w') as fd:
fd.write(api)
# this classpath stuff seems awful. Jsonify is hardcoded, passing a
# single string to subprocess.call is platform dependant, and shell=True
# can be a security vulnerability (if allowed to take user input).
# This just got a whole lot nastier
cmd = 'cd ' + pwd + '/..; /usr/bin/java -cp .:javaparser/javaparser-core/target/classes:$HOME/.m2/repository/com/cedarsoftware/json-io/4.3.0/json-io-4.3.0.jar jskparser.Jsonify ' + java_in + ' ' + json_out
ret = call(cmd, shell=True)
if ret != 0: exit('Problem parsing.')
return json_out
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SNeugber/fiftyone | fiftyone/core/patches.py | a50be47bbbf189e4bbdcd631b93c4c9cbf41c6b7 | """
Patches views.
| Copyright 2017-2021, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
from copy import deepcopy
import eta.core.utils as etau
import fiftyone.core.aggregations as foa
import fiftyone.core.dataset as fod
import fiftyone.core.fields as fof
import fiftyone.core.labels as fol
import fiftyone.core.media as fom
import fiftyone.core.sample as fos
import fiftyone.core.view as fov
_SINGLE_TYPES_MAP = {
fol.Detections: fol.Detection,
fol.Polylines: fol.Polyline,
}
_PATCHES_TYPES = (fol.Detections, fol.Polylines)
_NO_MATCH_ID = ""
class _PatchView(fos.SampleView):
@property
def _sample_id(self):
return self._doc.sample_id
def save(self):
super().save()
self._view._sync_source_sample(self)
class PatchView(_PatchView):
"""A patch in a :class:`PatchesView`.
:class:`PatchView` instances should not be created manually; they are
generated by iterating over :class:`PatchesView` instances.
Args:
doc: a :class:`fiftyone.core.odm.DatasetSampleDocument`
view: the :class:`PatchesView` that the patch belongs to
selected_fields (None): a set of field names that this view is
restricted to
excluded_fields (None): a set of field names that are excluded from
this view
filtered_fields (None): a set of field names of list fields that are
filtered in this view
"""
pass
class EvaluationPatchView(_PatchView):
"""A patch in an :class:`EvaluationPatchesView`.
:class:`EvaluationPatchView` instances should not be created manually; they
are generated by iterating over :class:`EvaluationPatchesView` instances.
Args:
doc: a :class:`fiftyone.core.odm.DatasetSampleDocument`
view: the :class:`EvaluationPatchesView` that the patch belongs to
selected_fields (None): a set of field names that this view is
restricted to
excluded_fields (None): a set of field names that are excluded from
this view
filtered_fields (None): a set of field names of list fields that are
filtered in this view
"""
pass
class _PatchesView(fov.DatasetView):
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
if _stages is None:
_stages = []
self._source_collection = source_collection
self._patches_stage = patches_stage
self._patches_dataset = patches_dataset
self.__stages = _stages
def __copy__(self):
return self.__class__(
self._source_collection,
deepcopy(self._patches_stage),
self._patches_dataset,
_stages=deepcopy(self.__stages),
)
@property
def _base_view(self):
return self.__class__(
self._source_collection,
self._patches_stage,
self._patches_dataset,
)
@property
def _dataset(self):
return self._patches_dataset
@property
def _root_dataset(self):
return self._source_collection._root_dataset
@property
def _stages(self):
return self.__stages
@property
def _all_stages(self):
return (
self._source_collection.view()._all_stages
+ [self._patches_stage]
+ self.__stages
)
@property
def _label_fields(self):
raise NotImplementedError("subclass must implement _label_fields")
@property
def _element_str(self):
return "patch"
@property
def _elements_str(self):
return "patches"
@property
def name(self):
return self.dataset_name + "-patches"
@classmethod
def _get_default_sample_fields(
cls, include_private=False, use_db_fields=False
):
fields = super()._get_default_sample_fields(
include_private=include_private, use_db_fields=use_db_fields
)
if use_db_fields:
return fields + ("_sample_id",)
return fields + ("sample_id",)
def set_values(self, field_name, *args, **kwargs):
field = field_name.split(".", 1)[0]
must_sync = field in self._label_fields
# The `set_values()` operation could change the contents of this view,
# so we first record the sample IDs that need to be synced
if must_sync and self._stages:
ids = self.values("_id")
else:
ids = None
super().set_values(field_name, *args, **kwargs)
if must_sync:
self._sync_source_field(field, ids=ids)
def save(self, fields=None):
"""Overwrites the object patches in the source dataset with the
contents of the view.
If this view contains any additional fields that were not extracted
from the source dataset, these fields are not saved.
.. warning::
This will permanently delete any omitted, filtered, or otherwise
modified patches from the source dataset.
Args:
fields (None): an optional field or list of fields to save. If
specified, only these fields are overwritten
"""
if etau.is_str(fields):
fields = [fields]
super().save(fields=fields)
if fields is None:
fields = self._label_fields
else:
fields = [l for l in fields if l in self._label_fields]
#
# IMPORTANT: we sync the contents of `_patches_dataset`, not `self`
# here because the `save()` call above updated the dataset, which means
# this view may no longer have the same contents (e.g., if `skip()` is
# involved)
#
self._sync_source_root(fields)
def reload(self):
self._root_dataset.reload()
#
# Regenerate the patches dataset
#
# This assumes that calling `load_view()` when the current patches
# dataset has been deleted will cause a new one to be generated
#
self._patches_dataset.delete()
_view = self._patches_stage.load_view(self._source_collection)
self._patches_dataset = _view._patches_dataset
def _sync_source_sample(self, sample):
for field in self._label_fields:
self._sync_source_sample_field(sample, field)
def _sync_source_sample_field(self, sample, field):
label_type = self._patches_dataset._get_label_field_type(field)
is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS)
doc = sample._doc.field_to_mongo(field)
if is_list_field:
doc = doc[label_type._LABEL_LIST_FIELD]
self._source_collection._set_labels_by_id(
field, [sample.sample_id], [doc]
)
def _sync_source_field(self, field, ids=None):
_, label_path = self._patches_dataset._get_label_field_path(field)
if ids is not None:
view = self._patches_dataset.mongo(
[{"$match": {"_id": {"$in": ids}}}]
)
else:
view = self._patches_dataset
sample_ids, docs = view.aggregate(
[foa.Values("sample_id"), foa.Values(label_path, _raw=True)]
)
self._source_collection._set_labels_by_id(field, sample_ids, docs)
def _sync_source_root(self, fields):
for field in fields:
self._sync_source_root_field(field)
def _sync_source_root_field(self, field):
_, id_path = self._get_label_field_path(field, "id")
label_path = id_path.rsplit(".", 1)[0]
#
# Sync label updates
#
sample_ids, docs, label_ids = self._patches_dataset.aggregate(
[
foa.Values("sample_id"),
foa.Values(label_path, _raw=True),
foa.Values(id_path, unwind=True),
]
)
self._source_collection._set_labels_by_id(field, sample_ids, docs)
#
# Sync label deletions
#
_, src_id_path = self._source_collection._get_label_field_path(
field, "id"
)
src_ids = self._source_collection.values(src_id_path, unwind=True)
delete_ids = set(src_ids) - set(label_ids)
if delete_ids:
self._source_collection._dataset.delete_labels(
ids=delete_ids, fields=field
)
def _get_ids_map(self, field):
label_type = self._patches_dataset._get_label_field_type(field)
is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS)
_, id_path = self._get_label_field_path(field, "id")
sample_ids, label_ids = self.values(["id", id_path])
ids_map = {}
if is_list_field:
for sample_id, _label_ids in zip(sample_ids, label_ids):
if not _label_ids:
continue
for label_id in _label_ids:
ids_map[label_id] = sample_id
else:
for sample_id, label_id in zip(sample_ids, label_ids):
if not label_id:
continue
ids_map[label_id] = sample_id
return ids_map
class PatchesView(_PatchesView):
"""A :class:`fiftyone.core.view.DatasetView` of patches from a
:class:`fiftyone.core.dataset.Dataset`.
Patches views contain an ordered collection of patch samples, each of which
contains a subset of a sample of the parent dataset corresponding to a
single object or logical grouping of of objects.
Patches retrieved from patches views are returned as :class:`PatchView`
objects.
Args:
source_collection: the
:class:`fiftyone.core.collections.SampleCollection` from which this
view was created
patches_stage: the :class:`fiftyone.core.stages.ToPatches` stage that
defines how the patches were extracted
patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves
the patches in this view
"""
_SAMPLE_CLS = PatchView
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
super().__init__(
source_collection, patches_stage, patches_dataset, _stages=_stages
)
self._patches_field = patches_stage.field
@property
def _label_fields(self):
return [self._patches_field]
@property
def patches_field(self):
"""The field from which the patches in this view were extracted."""
return self._patches_field
class EvaluationPatchesView(_PatchesView):
"""A :class:`fiftyone.core.view.DatasetView` containing evaluation patches
from a :class:`fiftyone.core.dataset.Dataset`.
Evalation patches views contain an ordered collection of evaluation
examples, each of which contains the ground truth and/or predicted labels
for a true positive, false positive, or false negative example from an
evaluation run on the underlying dataset.
Patches retrieved from patches views are returned as
:class:`EvaluationPatchView` objects.
Args:
source_collection: the
:class:`fiftyone.core.collections.SampleCollection` from which this
view was created
patches_stage: the :class:`fiftyone.core.stages.ToEvaluationPatches`
stage that defines how the patches were extracted
patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves
the patches in this view
"""
_SAMPLE_CLS = EvaluationPatchView
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
super().__init__(
source_collection, patches_stage, patches_dataset, _stages=_stages
)
eval_key = patches_stage.eval_key
eval_info = source_collection.get_evaluation_info(eval_key)
self._gt_field = eval_info.config.gt_field
self._pred_field = eval_info.config.pred_field
@property
def _label_fields(self):
return [self._gt_field, self._pred_field]
@property
def gt_field(self):
"""The ground truth field for the evaluation patches in this view."""
return self._gt_field
@property
def pred_field(self):
"""The predictions field for the evaluation patches in this view."""
return self._pred_field
def make_patches_dataset(
sample_collection, field, keep_label_lists=False, name=None
):
"""Creates a dataset that contains one sample per object patch in the
specified field of the collection.
Fields other than ``field`` and the default sample fields will not be
included in the returned dataset. A ``sample_id`` field will be added that
records the sample ID from which each patch was taken.
Args:
sample_collection: a
:class:`fiftyone.core.collections.SampleCollection`
field: the patches field, which must be of type
:class:`fiftyone.core.labels.Detections` or
:class:`fiftyone.core.labels.Polylines`
keep_label_lists (False): whether to store the patches in label list
fields of the same type as the input collection rather than using
their single label variants
name (None): a name for the returned dataset
Returns:
a :class:`fiftyone.core.dataset.Dataset`
"""
if keep_label_lists:
field_type = sample_collection._get_label_field_type(field)
else:
field_type = _get_single_label_field_type(sample_collection, field)
dataset = fod.Dataset(name, _patches=True)
dataset.media_type = fom.IMAGE
dataset.add_sample_field(
"sample_id", fof.ObjectIdField, db_field="_sample_id"
)
dataset.add_sample_field(
field, fof.EmbeddedDocumentField, embedded_doc_type=field_type
)
patches_view = _make_patches_view(
sample_collection, field, keep_label_lists=keep_label_lists
)
_write_samples(dataset, patches_view)
return dataset
def _get_single_label_field_type(sample_collection, field):
label_type = sample_collection._get_label_field_type(field)
if label_type not in _SINGLE_TYPES_MAP:
raise ValueError("Unsupported label field type %s" % label_type)
return _SINGLE_TYPES_MAP[label_type]
def make_evaluation_dataset(sample_collection, eval_key, name=None):
"""Creates a dataset based on the results of the evaluation with the given
key that contains one sample for each true positive, false positive, and
false negative example in the input collection, respectively.
True positive examples will result in samples with both their ground truth
and predicted fields populated, while false positive/negative examples will
only have one of their corresponding predicted/ground truth fields
populated, respectively.
If multiple predictions are matched to a ground truth object (e.g., if the
evaluation protocol includes a crowd attribute), then all matched
predictions will be stored in the single sample along with the ground truth
object.
The returned dataset will also have top-level ``type`` and ``iou`` fields
populated based on the evaluation results for that example, as well as a
``sample_id`` field recording the sample ID of the example, and a ``crowd``
field if the evaluation protocol defines a crowd attribute.
.. note::
The returned dataset will contain patches for the contents of the input
collection, which may differ from the view on which the ``eval_key``
evaluation was performed. This may exclude some labels that were
evaluated and/or include labels that were not evaluated.
If you would like to see patches for the exact view on which an
evaluation was performed, first call
:meth:`load_evaluation_view() <fiftyone.core.collections.SampleCollection.load_evaluation_view>`
to load the view and then convert to patches.
Args:
sample_collection: a
:class:`fiftyone.core.collections.SampleCollection`
eval_key: an evaluation key that corresponds to the evaluation of
ground truth/predicted fields that are of type
:class:`fiftyone.core.labels.Detections` or
:class:`fiftyone.core.labels.Polylines`
name (None): a name for the returned dataset
Returns:
a :class:`fiftyone.core.dataset.Dataset`
"""
# Parse evaluation info
eval_info = sample_collection.get_evaluation_info(eval_key)
pred_field = eval_info.config.pred_field
gt_field = eval_info.config.gt_field
if hasattr(eval_info.config, "iscrowd"):
crowd_attr = eval_info.config.iscrowd
else:
crowd_attr = None
pred_type = sample_collection._get_label_field_type(pred_field)
gt_type = sample_collection._get_label_field_type(gt_field)
# Setup dataset with correct schema
dataset = fod.Dataset(name, _patches=True)
dataset.media_type = fom.IMAGE
dataset.add_sample_field(
pred_field, fof.EmbeddedDocumentField, embedded_doc_type=pred_type
)
dataset.add_sample_field(
gt_field, fof.EmbeddedDocumentField, embedded_doc_type=gt_type
)
dataset.add_sample_field(
"sample_id", fof.ObjectIdField, db_field="_sample_id"
)
dataset.add_sample_field("type", fof.StringField)
dataset.add_sample_field("iou", fof.FloatField)
if crowd_attr is not None:
dataset.add_sample_field("crowd", fof.BooleanField)
# Add ground truth patches
gt_view = _make_eval_view(
sample_collection, eval_key, gt_field, crowd_attr=crowd_attr
)
_write_samples(dataset, gt_view)
# Merge matched predictions
_merge_matched_labels(dataset, sample_collection, eval_key, pred_field)
# Add unmatched predictions
unmatched_pred_view = _make_eval_view(
sample_collection, eval_key, pred_field, skip_matched=True
)
_add_samples(dataset, unmatched_pred_view)
return dataset
def _make_patches_view(sample_collection, field, keep_label_lists=False):
if sample_collection._is_frames:
raise ValueError(
"Creating patches views into frame views is not yet supported"
)
if sample_collection._is_frame_field(field):
raise ValueError(
"Frame label patches cannot be directly extracted; you must first "
"convert your video dataset to frames via `to_frames()`"
)
label_type = sample_collection._get_label_field_type(field)
if issubclass(label_type, _PATCHES_TYPES):
list_field = field + "." + label_type._LABEL_LIST_FIELD
else:
raise ValueError(
"Invalid label field type %s. Extracting patches is only "
"supported for the following types: %s"
% (label_type, _PATCHES_TYPES)
)
pipeline = [
{
"$project": {
"_id": True,
"_sample_id": "$_id",
"_media_type": True,
"filepath": True,
"metadata": True,
"tags": True,
field + "._cls": True,
list_field: True,
}
},
{"$unwind": "$" + list_field},
{"$set": {"_rand": {"$rand": {}}}},
{"$set": {"_id": "$" + list_field + "._id"}},
]
if keep_label_lists:
pipeline.append({"$set": {list_field: ["$" + list_field]}})
else:
pipeline.append({"$set": {field: "$" + list_field}})
return sample_collection.mongo(pipeline)
def _make_eval_view(
sample_collection, eval_key, field, skip_matched=False, crowd_attr=None
):
eval_type = field + "." + eval_key
eval_id = field + "." + eval_key + "_id"
eval_iou = field + "." + eval_key + "_iou"
view = _make_patches_view(sample_collection, field)
if skip_matched:
view = view.mongo(
[
{
"$match": {
"$expr": {
"$or": [
{"$eq": ["$" + eval_id, _NO_MATCH_ID]},
{"$not": {"$gt": ["$" + eval_id, None]}},
]
}
}
}
]
)
view = view.mongo(
[{"$set": {"type": "$" + eval_type, "iou": "$" + eval_iou}}]
)
if crowd_attr is not None:
crowd_path1 = "$" + field + "." + crowd_attr
# @todo remove Attributes usage
crowd_path2 = "$" + field + ".attributes." + crowd_attr + ".value"
view = view.mongo(
[
{
"$set": {
"crowd": {
"$cond": {
"if": {"$gt": [crowd_path1, None]},
"then": {"$toBool": crowd_path1},
"else": {
"$cond": {
"if": {"$gt": [crowd_path2, None]},
"then": {"$toBool": crowd_path2},
"else": None,
}
},
}
}
}
}
]
)
return _upgrade_labels(view, field)
def _upgrade_labels(view, field):
tmp_field = "_" + field
label_type = view._get_label_field_type(field)
return view.mongo(
[
{"$set": {tmp_field: "$" + field}},
{"$unset": field},
{
"$set": {
field: {
"_cls": label_type.__name__,
label_type._LABEL_LIST_FIELD: ["$" + tmp_field],
}
}
},
{"$unset": tmp_field},
]
)
def _merge_matched_labels(dataset, src_collection, eval_key, field):
field_type = src_collection._get_label_field_type(field)
list_field = field + "." + field_type._LABEL_LIST_FIELD
eval_id = eval_key + "_id"
eval_field = list_field + "." + eval_id
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.extend(
[
{"$project": {list_field: True}},
{"$unwind": "$" + list_field},
{
"$match": {
"$expr": {
"$and": [
{"$gt": ["$" + eval_field, None]},
{"$ne": ["$" + eval_field, _NO_MATCH_ID]},
]
}
}
},
{
"$group": {
"_id": {"$toObjectId": "$" + eval_field},
"_labels": {"$push": "$" + list_field},
}
},
{
"$project": {
field: {
"_cls": field_type.__name__,
field_type._LABEL_LIST_FIELD: "$_labels",
}
},
},
{
"$merge": {
"into": dataset._sample_collection_name,
"on": "_id",
"whenMatched": "merge",
"whenNotMatched": "discard",
}
},
]
)
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
def _write_samples(dataset, src_collection):
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.append({"$out": dataset._sample_collection_name})
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
def _add_samples(dataset, src_collection):
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.append(
{
"$merge": {
"into": dataset._sample_collection_name,
"on": "_id",
"whenMatched": "keepExisting",
"whenNotMatched": "insert",
}
}
)
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
| [((439, 14, 439, 46), 'fiftyone.core.dataset.Dataset', 'fod.Dataset', (), '', True, 'import fiftyone.core.dataset as fod\n'), ((522, 14, 522, 46), 'fiftyone.core.dataset.Dataset', 'fod.Dataset', (), '', True, 'import fiftyone.core.dataset as fod\n'), ((188, 11, 188, 30), 'eta.core.utils.is_str', 'etau.is_str', ({(188, 23, 188, 29): 'fields'}, {}), '(fields)', True, 'import eta.core.utils as etau\n'), ((94, 12, 94, 41), 'copy.deepcopy', 'deepcopy', ({(94, 21, 94, 40): 'self._patches_stage'}, {}), '(self._patches_stage)', False, 'from copy import deepcopy\n'), ((96, 20, 96, 43), 'copy.deepcopy', 'deepcopy', ({(96, 29, 96, 42): 'self.__stages'}, {}), '(self.__stages)', False, 'from copy import deepcopy\n'), ((248, 13, 248, 36), 'fiftyone.core.aggregations.Values', 'foa.Values', ({(248, 24, 248, 35): '"""sample_id"""'}, {}), "('sample_id')", True, 'import fiftyone.core.aggregations as foa\n'), ((248, 38, 248, 71), 'fiftyone.core.aggregations.Values', 'foa.Values', (), '', True, 'import fiftyone.core.aggregations as foa\n'), ((267, 16, 267, 39), 'fiftyone.core.aggregations.Values', 'foa.Values', ({(267, 27, 267, 38): '"""sample_id"""'}, {}), "('sample_id')", True, 'import fiftyone.core.aggregations as foa\n'), ((268, 16, 268, 49), 'fiftyone.core.aggregations.Values', 'foa.Values', (), '', True, 'import fiftyone.core.aggregations as foa\n'), ((269, 16, 269, 48), 'fiftyone.core.aggregations.Values', 'foa.Values', (), '', True, 'import fiftyone.core.aggregations as foa\n')] |
ocesaulo/cookiecutter-ocn_sci | {{cookiecutter.repo_name}}/setup.py | d41e826f56ba67cfde878ffc8188d497214a5f5b | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""The setup script."""
from setuptools import setup, find_packages
with open('README.rst') as readme_file:
readme = readme_file.read()
{%- set license_classifiers = {
'MIT license': 'License :: OSI Approved :: MIT License',
'BSD license': 'License :: OSI Approved :: BSD License',
'ISC license': 'License :: OSI Approved :: ISC License (ISCL)',
'Apache Software License 2.0': 'License :: OSI Approved :: Apache Software License',
'GNU General Public License v3': 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)'
} %}
# get the dependencies and installs
with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:
all_reqs = f.read().split('\n')
install_requires = [x.strip() for x in all_reqs if 'git+' not in x]
dependency_links = [x.strip().replace('git+', '') for x in all_reqs if x.startswith('git+')]
tests_requirements = ['pytest'],
setup_requirements = ['pytest-runner']
requirements = [
# package requirements go here
]
setup(
name='{{ cookiecutter.repo_name }}',
version=__version__,
description="{{ cookiecutter.project_short_description }}",
long_description=readme,
author="{{ cookiecutter.full_name.replace('\"', '\\\"') }}",
author_email='{{ cookiecutter.email }}',
url='https://github.com/{{ cookiecutter.github_username }}/{{ cookiecutter.repo_name }}',
packages=find_packages(include=['{{ cookiecutter.repo_name }}'],
exclude=('docs', 'tests*',)),
{%- if cookiecutter.open_source_license in license_classifiers %}
license="{{ cookiecutter.open_source_license }}",
{%- endif %}
install_requires=install_requires,
dependency_links=dependency_links,
setup_requires=setup_requirements,
test_suite='tests',
tests_require=test_requirements,
keywords='{{ cookiecutter.repo_name }}',
classifiers=[
'Programming Language :: Python :: 3.6',
]
)
| [] |
zopefoundation/zope.app.debug | src/zope/app/debug/debug.py | 4f31e98f6a633f089bf132dd55cb3ead0270887b | ##############################################################################
#
# Copyright (c) 2002 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""Code to initialize the application server
"""
from __future__ import print_function
__docformat__ = 'restructuredtext'
import base64
import time
import sys
from pdb import Pdb
from io import BytesIO
from zope.publisher.publish import publish as _publish, debug_call
from zope.publisher.browser import TestRequest, setDefaultSkin
from zope.app.publication.browser import BrowserPublication
from zope.app.appsetup import config, database
try:
from time import process_time as time_process_time # pragma: PY3
except ImportError:
from time import clock as time_process_time # pragma: PY2
try:
import urllib.parse as urllib # pragma: PY3
except ImportError:
import urllib # pragma: PY2
try:
text_type = unicode # pragma: PY2
except NameError:
text_type = str # pragma: PY3
class Debugger(object):
pdb = Pdb
def __init__(self, db=None, config_file=None, stdout=None):
if db is None and config_file is None:
db = 'Data.fs'
config_file = 'site.zcml'
if config_file is not None:
config(config_file)
self.db = database(db)
self.stdout = stdout
@classmethod
def fromDatabase(cls, db):
inst = cls.__new__(cls)
inst.db = db
return inst
def root(self):
"""Get the top-level application object
The object returned is connected to an open database connection.
"""
from zope.app.publication.zopepublication import ZopePublication
return self.db.open().root()[ZopePublication.root_name]
def _request(self,
path='/', stdin='', basic=None,
environment=None, form=None,
request=None, publication=BrowserPublication):
"""Create a request
"""
env = {}
if isinstance(stdin, text_type):
stdin = stdin.encode("utf-8")
if isinstance(stdin, bytes):
stdin = BytesIO(stdin)
p = path.split('?')
if len(p) == 1:
env['PATH_INFO'] = p[0]
elif len(p) == 2:
env['PATH_INFO'], env['QUERY_STRING'] = p
else:
raise ValueError("Too many ?s in path", path)
env['PATH_INFO'] = urllib.unquote(env['PATH_INFO'])
if environment is not None:
env.update(environment)
if basic:
basic_bytes = basic.encode('ascii') if not isinstance(
basic, bytes) else basic
basic64_bytes = base64.b64encode(basic_bytes)
basic64 = basic64_bytes.decode('ascii').strip()
env['HTTP_AUTHORIZATION'] = "Basic %s" % basic64
pub = publication(self.db)
if request is not None:
request = request(stdin, env)
else:
request = TestRequest(stdin, env)
setDefaultSkin(request)
request.setPublication(pub)
if form:
request.form.update(form)
return request
def publish(self, path='/', stdin='', *args, **kw):
t, pt = time.time(), time_process_time()
request = self._request(path, stdin, *args, **kw)
# agroszer: 2008.feb.1.: if a retry occurs in the publisher,
# the response will be LOST, so we must accept the returned request
request = _publish(request)
getStatus = getattr(request.response, 'getStatus', lambda: None)
headers = sorted(request.response.getHeaders())
print(
'Status %s\r\n%s\r\n\r\n%s' % (
request.response.getStatusString(),
'\r\n'.join([("%s: %s" % h) for h in headers]),
request.response.consumeBody(),
), file=self.stdout or sys.stdout)
return time.time() - t, time_process_time() - pt, getStatus()
def run(self, *args, **kw):
t, pt = time.time(), time_process_time()
request = self._request(*args, **kw)
# agroszer: 2008.feb.1.: if a retry occurs in the publisher,
# the response will be LOST, so we must accept the returned request
request = _publish(request, handle_errors=False)
getStatus = getattr(request.response, 'getStatus', lambda: None)
return time.time() - t, time_process_time() - pt, getStatus()
def debug(self, *args, **kw):
out = self.stdout or sys.stdout
class ZopePdb(self.Pdb):
done_pub = False
done_ob = False
def do_pub(self, arg):
if self.done_pub:
print('pub already done.', file=out)
return
self.do_s('')
self.do_s('')
self.do_c('')
self.done_pub = True
def do_ob(self, arg):
if self.done_ob:
print('ob already done.', file=out)
return
self.do_pub('')
self.do_c('')
self.done_ob = True
dbg = ZopePdb()
request = self._request(*args, **kw)
fbreak(dbg, _publish)
fbreak(dbg, debug_call)
print('* Type c<cr> to jump to published object call.',
file=out)
dbg.runcall(_publish, request)
return dbg
def getlineno(code):
return code.co_firstlineno
def fbreak(db, meth):
try:
meth = meth.__func__
except AttributeError:
pass
code = meth.__code__
lineno = getlineno(code)
filename = code.co_filename
db.set_break(filename, lineno)
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terryli710/SIIM-ACR-Pneumothorax-Classification | transfer_learning.py | 8b278a9885b71c919d7064b2df42863b53f7adf3 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 18 22:42:54 2020
@author: mike
"""
import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers
from sklearn.preprocessing import OneHotEncoder
from skimage.transform import resize
import matplotlib.pyplot as plt
train_data = np.load("train_data.npy")
x_data = np.zeros((210,204,204,3))
y_data = np.zeros(210)
for i in range(210):
img = train_data[i,1:].reshape(1024,1024)
img_resized = resize(img,(204,204))
y_data[i] = train_data[i,0]
x_data[i,:,:,0] = img_resized.astype(int)
x_data[i,:,:,1] = img_resized.astype(int)
x_data[i,:,:,2] = img_resized.astype(int)
x_train, x_test, y_train, y_test = train_test_split(
x_data, y_data, test_size=0.2, random_state=42)
y_train = OneHotEncoder().fit_transform(y_train.reshape(-1,1)).toarray()
y_test = OneHotEncoder().fit_transform(y_test.reshape(-1,1)).toarray()
base_model = VGG16(include_top=False, weights='vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
input_shape=(204, 204, 3))
base_model.trainable = False
inputs = tf.keras.Input(shape=(204, 204, 3))
x = base_model(inputs)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
outputs = tf.keras.layers.Dense(2, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.summary()
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),loss="binary_crossentropy",metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=16, epochs=5)
pred = model.predict(x_train)
score = model.evaluate(x_test, y_test, verbose=0)
print(score[0],score[1]) | [((21, 13, 21, 38), 'numpy.load', 'np.load', ({(21, 21, 21, 37): '"""train_data.npy"""'}, {}), "('train_data.npy')", True, 'import numpy as np\n'), ((23, 9, 23, 34), 'numpy.zeros', 'np.zeros', ({(23, 18, 23, 33): '(210, 204, 204, 3)'}, {}), '((210, 204, 204, 3))', True, 'import numpy as np\n'), ((24, 9, 24, 22), 'numpy.zeros', 'np.zeros', ({(24, 18, 24, 21): '210'}, {}), '(210)', True, 'import numpy as np\n'), ((34, 35, 35, 55), 'sklearn.model_selection.train_test_split', 'train_test_split', (), '', False, 'from sklearn.model_selection import train_test_split\n'), ((45, 13, 46, 45), 'tensorflow.keras.applications.VGG16', 'VGG16', (), '', False, 'from tensorflow.keras.applications import VGG16\n'), ((49, 9, 49, 44), 'tensorflow.keras.Input', 'tf.keras.Input', (), '', True, 'import tensorflow as tf\n'), ((55, 8, 55, 36), 'tensorflow.keras.Model', 'keras.Model', ({(55, 20, 55, 26): 'inputs', (55, 28, 55, 35): 'outputs'}, {}), '(inputs, outputs)', False, 'from tensorflow import keras\n'), ((28, 18, 28, 39), 'skimage.transform.resize', 'resize', ({(28, 25, 28, 28): 'img', (28, 29, 28, 38): '(204, 204)'}, {}), '(img, (204, 204))', False, 'from skimage.transform import resize\n'), ((51, 4, 51, 29), 'tensorflow.keras.layers.Flatten', 'tf.keras.layers.Flatten', ({}, {}), '()', True, 'import tensorflow as tf\n'), ((52, 4, 52, 49), 'tensorflow.keras.layers.Dense', 'tf.keras.layers.Dense', (), '', True, 'import tensorflow as tf\n'), ((53, 4, 53, 48), 'tensorflow.keras.layers.Dense', 'tf.keras.layers.Dense', (), '', True, 'import tensorflow as tf\n'), ((54, 10, 54, 56), 'tensorflow.keras.layers.Dense', 'tf.keras.layers.Dense', (), '', True, 'import tensorflow as tf\n'), ((61, 24, 61, 68), 'tensorflow.keras.optimizers.SGD', 'tf.keras.optimizers.SGD', (), '', True, 'import tensorflow as tf\n'), ((39, 10, 39, 25), 'sklearn.preprocessing.OneHotEncoder', 'OneHotEncoder', ({}, {}), '()', False, 'from sklearn.preprocessing import OneHotEncoder\n'), ((40, 9, 40, 24), 'sklearn.preprocessing.OneHotEncoder', 'OneHotEncoder', ({}, {}), '()', False, 'from sklearn.preprocessing import OneHotEncoder\n')] |
erexer/polyaxon | core/tests/test_polyflow/test_workflows/test_hyperband.py | be14dae1ed56d568983388736bcdaf27a7baa4a4 | #!/usr/bin/python
#
# Copyright 2018-2020 Polyaxon, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from marshmallow.exceptions import ValidationError
from tests.utils import BaseTestCase, assert_equal_dict
from polyaxon.polyflow.matrix import V1Hyperband
from polyaxon.polyflow.optimization import V1Optimization, V1OptimizationMetric
@pytest.mark.workflow_mark
class TestWorkflowV1Hyperbands(BaseTestCase):
def test_hyperband_config(self):
config_dict = {
"kind": "hyperband",
"maxIterations": 10,
"eta": 3,
"resource": {"name": "steps", "type": "int"},
"resume": False,
"metric": V1OptimizationMetric(
name="loss", optimization=V1Optimization.MINIMIZE
).to_dict(),
"params": {"lr": {"kind": "choice", "value": [[0.1], [0.9]]}},
}
config = V1Hyperband.from_dict(config_dict)
assert_equal_dict(config.to_dict(), config_dict)
# Raises for negative values
config_dict["maxIterations"] = 0
with self.assertRaises(ValidationError):
V1Hyperband.from_dict(config_dict)
config_dict["maxIterations"] = -0.5
with self.assertRaises(ValidationError):
V1Hyperband.from_dict(config_dict)
config_dict["maxIterations"] = 3
# Add numRuns percent
config_dict["eta"] = -0.5
with self.assertRaises(ValidationError):
V1Hyperband.from_dict(config_dict)
config_dict["eta"] = 2.9
config = V1Hyperband.from_dict(config_dict)
assert_equal_dict(config.to_dict(), config_dict)
| [((40, 17, 40, 51), 'polyaxon.polyflow.matrix.V1Hyperband.from_dict', 'V1Hyperband.from_dict', ({(40, 39, 40, 50): 'config_dict'}, {}), '(config_dict)', False, 'from polyaxon.polyflow.matrix import V1Hyperband\n'), ((59, 17, 59, 51), 'polyaxon.polyflow.matrix.V1Hyperband.from_dict', 'V1Hyperband.from_dict', ({(59, 39, 59, 50): 'config_dict'}, {}), '(config_dict)', False, 'from polyaxon.polyflow.matrix import V1Hyperband\n'), ((46, 12, 46, 46), 'polyaxon.polyflow.matrix.V1Hyperband.from_dict', 'V1Hyperband.from_dict', ({(46, 34, 46, 45): 'config_dict'}, {}), '(config_dict)', False, 'from polyaxon.polyflow.matrix import V1Hyperband\n'), ((50, 12, 50, 46), 'polyaxon.polyflow.matrix.V1Hyperband.from_dict', 'V1Hyperband.from_dict', ({(50, 34, 50, 45): 'config_dict'}, {}), '(config_dict)', False, 'from polyaxon.polyflow.matrix import V1Hyperband\n'), ((56, 12, 56, 46), 'polyaxon.polyflow.matrix.V1Hyperband.from_dict', 'V1Hyperband.from_dict', ({(56, 34, 56, 45): 'config_dict'}, {}), '(config_dict)', False, 'from polyaxon.polyflow.matrix import V1Hyperband\n'), ((35, 22, 37, 13), 'polyaxon.polyflow.optimization.V1OptimizationMetric', 'V1OptimizationMetric', (), '', False, 'from polyaxon.polyflow.optimization import V1Optimization, V1OptimizationMetric\n')] |
fatimatswanya/fatimaCSC102 | Class Work oop.py | cab70bd696d39a9e16bcb57e0180e872be4f49bc |
class Student:
studentLevel = 'first year computer science 2020/2021 session'
studentCounter = 0
registeredCourse='csc102'
def __init__(self, thename, thematricno, thesex,thehostelname,theage,thecsc102examscore):
self.name = thename
self.matricno = thematricno
self.sex = thesex
self.hostelname =thehostelname
self.age=theage
self.csc102examscore=thecsc102examscore
Student.studentCounter = Student.studentCounter + 1
def getName(self):
return self.name
def setName(self, thenewName):
self.name = thenewName
def agedeterminer(self):
if self.age>16:
print('Student is above 16')
def finalscore(self):
if self.csc102examscore < 45:
print('You will carryover this course, sorry')
else:
print('You have passed')
@classmethod
def course():
print(f'Students registered course is {Student.registeredCourse}')
@staticmethod
def PAUNanthem():
print('Pau, here we come, Pau, here we come ')
@staticmethod
def ODDorEVEN(num):
if num % 2==0:
print('Number is even')
else:
print('Number is odd')
@classmethod
def studentnum(cls):
print(Student.studentCounter)
studendt1 = Student('James Kaka', '021074', 'M','Amethyst','16', '49')
print(studendt1.getName())
studendt1.setName('James Gaga')
print(studendt1.getName())
Student.PAUNanthem() | [] |
Fozar/clickhouse-sqlalchemy | clickhouse_sqlalchemy/drivers/reflection.py | 88fd630856655cc470430b365dce7e85516abf62 | from sqlalchemy.engine import reflection
from clickhouse_sqlalchemy import Table, engines
class ClickHouseInspector(reflection.Inspector):
def reflect_table(self, table, *args, **kwargs):
# This check is necessary to support direct instantiation of
# `clickhouse_sqlalchemy.Table` and then reflection of it.
if not isinstance(table, Table):
table.metadata.remove(table)
ch_table = Table._make_from_standard(
table, _extend_on=kwargs.get('_extend_on')
)
else:
ch_table = table
super(ClickHouseInspector, self).reflect_table(
ch_table, *args, **kwargs
)
with self._operation_context() as conn:
schema = conn.schema_for_object(ch_table)
self._reflect_engine(ch_table.name, schema, ch_table)
def _reflect_engine(self, table_name, schema, table):
should_reflect = (
self.dialect.supports_engine_reflection and
self.dialect.engine_reflection
)
if not should_reflect:
return
engine_cls_by_name = {e.__name__: e for e in engines.__all__}
e = self.get_engine(table_name, schema=table.schema)
if not e:
raise ValueError("Cannot find engine for table '%s'" % table_name)
engine_cls = engine_cls_by_name.get(e['engine'])
if engine_cls is not None:
engine = engine_cls.reflect(table, **e)
engine._set_parent(table)
else:
table.engine = None
def get_engine(self, table_name, schema=None, **kw):
with self._operation_context() as conn:
return self.dialect.get_engine(
conn, table_name, schema=schema, info_cache=self.info_cache,
**kw
)
| [] |
abdul-khalid/pydisque | tests/test_disque.py | a9b5caa6dac0621a0174d168f4a04c88d0e2f8b5 | """
Unit Tests for the pydisque module.
Currently, most of these tests require a fresh instance of
Disque to be valid and pass.
"""
import unittest
import json
import time
import random
import six
from pydisque.client import Client
from redis.exceptions import ResponseError
class TestDisque(unittest.TestCase):
"""TestCase class for pydisque."""
testID = None
def setUp(self):
"""Setup the tests."""
self.client = Client(['localhost:7711'])
self.client.connect()
self.testID = "%d.%d" % (time.time(),
random.randint(1000, 1000000))
def test_publish_and_receive(self):
"""Test the most important functions of pydisque."""
t1 = str(time.time())
self.client.add_job("test_q", t1, timeout=100)
jobs = self.client.get_job(['test_q'])
assert len(jobs) == 1
for queue_name, job_id, job in jobs:
assert job == six.b(t1)
self.client.ack_job(job_id)
assert len(self.client.get_job(['test_q'], timeout=100)) == 0
def test_nack(self):
"""Fetch the queue, return a job, check that it's back."""
t1 = str(time.time())
queuename = "test_nack." + self.testID
self.client.add_job(queuename, str(t1), timeout=100)
jobs = self.client.get_job([queuename])
# NACK the first read
assert len(jobs) == 1
for queue_name, job_id, job in jobs:
assert len(jobs) == 1
assert job == six.b(t1)
self.client.nack_job(job_id)
# this time ACK it
jobs = self.client.get_job([queuename])
assert len(jobs) == 1
for queue_name, job_id, job in jobs:
assert job == six.b(t1)
self.client.ack_job(job_id)
assert len(self.client.get_job([queuename], timeout=100)) == 0
def test_qpeek(self):
"""
Test qpeek.
Ran into some problems with an ENQUEUE/DEQUEUE test that
was using qpeek, checking core functionality of qpeek().
"""
queuename = "test_qpeek-%s" % self.testID
job_id = self.client.add_job(queuename, "Peek A Boo")
peeked = self.client.qpeek(queuename, 1)
assert peeked[0][1] == job_id
def test_qscan(self):
"""
Test the qscan function.
This test relies on add_job() being functional, and
the local disque not being a disque proxy to a mesh.
TODO: unique the queues with self.testID.
"""
t1 = str(time.time())
self.client.add_job("q1", t1, timeout=100)
self.client.add_job("q2", t1, timeout=100)
qb = self.client.qscan()
assert qb[0]
assert qb[1]
assert six.b("q1") in qb[1]
assert six.b("q2") in qb[1]
def test_jscan(self):
"""Simple test of the jscan function."""
t1 = time.time()
queuename = "test_jscan-%s" % self.testID
j1 = self.client.add_job(queuename, str(t1), timeout=100)
jerbs = self.client.jscan(queue=queuename)
assert j1 in jerbs[1]
def test_del_job(self):
"""Simple test of del_job, needs qpeek.
FIXME: This function has grown ugly.
"""
t1 = time.time()
queuename = "test_del_job-%s" % self.testID
j1 = self.client.add_job(queuename, str(t1))
jerbs = self.client.qpeek(queuename, 1)
jlist = []
for item in jerbs:
jlist.append(item[1])
assert j1 in jlist
self.client.del_job(j1)
jerbs = self.client.qpeek(queuename, 1)
jlist = []
for item in jerbs:
jlist.append(item[1])
assert j1 not in jerbs
def test_qlen(self):
"""Simple test of qlen."""
queuename = "test_qlen-%s" % self.testID
lengthOfTest = 100
test_job = "Useless Job."
for x in range(lengthOfTest):
self.client.add_job(queuename, test_job)
assert self.client.qlen(queuename) == lengthOfTest
def test_qstat(self):
"""Testing QSTAT (default behavior)."""
queuename = "test_qstat-%s" % self.testID
testqueue = ["a", "b", "c"]
for x in testqueue:
self.client.add_job(queuename, x)
stat = self.client.qstat(queuename)
# check the basics
assert 'jobs-in' in stat
assert 'jobs-out' in stat
def test_qstat_dict(self):
"""Testing QSTAT's (new dict behavior)."""
queuename = "test_qstat_dict-%s" % self.testID
testqueue = ["a", "b", "c"]
for x in testqueue:
self.client.add_job(queuename, x)
stat = self.client.qstat(queuename, True)
assert stat.get('jobs-in', None) is not None
assert stat.get('jobs-out', None) is not None
def test_shownack(self):
"""Test that NACK and SHOW work appropriately."""
queuename = "test_show-%s" % self.testID
test_job = "Show me."
self.client.add_job(queuename, test_job)
jobs = self.client.get_job([queuename])
for queue_name, job_id, job in jobs:
self.client.nack_job(job_id)
shown = self.client.show(job_id, True)
assert shown.get('body') == test_job
assert shown.get('nacks') == 1
def test_pause(self):
"""Test that a PAUSE message is acknowledged."""
queuename = "test_show-%s" % self.testID
test_job = "Jerbs, they are a thing"
self.client.pause(queuename, kw_in=True)
try:
job_id = self.client.add_job(queuename, test_job)
except ResponseError:
pass
# can we add a job again?
self.client.pause(queuename, kw_none=True)
job_id = self.client.add_job(queuename, test_job)
jobs = self.client.get_job([queuename])
# TODO(canardleteer): add a test of PAUSE SHOW
def test_get_job(self):
queue_name = "test_get_job." + self.testID
job = str(time.time())
job_id = self.client.add_job(queue_name, job)
expected = [(queue_name, job_id, job)]
got = self.client.get_job([queue_name], withcounters=False)
assert expected == got
def test_get_job_withcounters(self):
queue_name = "test_get_job." + self.testID
job = str(time.time())
job_id = self.client.add_job(queue_name, job)
nacks = 0
additional_deliveries = 0
expected = [(queue_name, job_id, job, nacks, additional_deliveries)]
got = self.client.get_job([queue_name], withcounters=True)
assert expected == got
if __name__ == '__main__':
unittest.main()
| [((232, 4, 232, 19), 'unittest.main', 'unittest.main', ({}, {}), '()', False, 'import unittest\n'), ((25, 22, 25, 48), 'pydisque.client.Client', 'Client', ({(25, 29, 25, 47): "['localhost:7711']"}, {}), "(['localhost:7711'])", False, 'from pydisque.client import Client\n'), ((98, 13, 98, 24), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((110, 13, 110, 24), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((32, 17, 32, 28), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((43, 17, 43, 28), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((83, 17, 83, 28), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((93, 15, 93, 26), 'six.b', 'six.b', ({(93, 21, 93, 25): '"""q1"""'}, {}), "('q1')", False, 'import six\n'), ((94, 15, 94, 26), 'six.b', 'six.b', ({(94, 21, 94, 25): '"""q2"""'}, {}), "('q2')", False, 'import six\n'), ((212, 18, 212, 29), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((222, 18, 222, 29), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((27, 33, 27, 44), 'time.time', 'time.time', ({}, {}), '()', False, 'import time\n'), ((28, 33, 28, 62), 'random.randint', 'random.randint', ({(28, 48, 28, 52): '(1000)', (28, 54, 28, 61): '(1000000)'}, {}), '(1000, 1000000)', False, 'import random\n'), ((37, 26, 37, 35), 'six.b', 'six.b', ({(37, 32, 37, 34): 't1'}, {}), '(t1)', False, 'import six\n'), ((51, 26, 51, 35), 'six.b', 'six.b', ({(51, 32, 51, 34): 't1'}, {}), '(t1)', False, 'import six\n'), ((57, 26, 57, 35), 'six.b', 'six.b', ({(57, 32, 57, 34): 't1'}, {}), '(t1)', False, 'import six\n')] |
samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers | src/runner.py | 619c5c0b17438d1014f7ca7e4ce13cc44c45de3c | # -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ runner.py ]
# Synopsis [ main program that runs the 'Naive Bayes' and 'Decision Tree' training / testing ]
# Author [ Ting-Wei Liu (Andi611) ]
# Copyright [ Copyleft(c), NTUEE, NTU, Taiwan ]
"""*********************************************************************************************"""
###############
# IMPORTATION #
###############
import os
import csv
import argparse
import numpy as np
from data_loader import data_loader
from classifiers import naive_bayes_runner
from classifiers import decision_tree_runner
##################
# CONFIGURATIONS #
##################
def get_config():
parser = argparse.ArgumentParser(description='descrip_msg')
classifier = parser.add_argument_group('classifier')
classifier.add_argument('--classifier', type=str, default='', help='classifier to be specified by user')
classifier.add_argument('--naive_bayes', action='store_true', help='enable Naive Bayes classification mode')
classifier.add_argument('--decision_tree', action='store_true', help='enable Decision Tree classification mode')
mode_args = parser.add_argument_group('mode')
mode_args.add_argument('--search_opt', action='store_true', help='search for optimal parameters for classifiers')
mode_args.add_argument('--run_all', action='store_true', help='run all distribution assumption for the Naive Bayes classifier')
mode_args.add_argument('--visualize_tree', action='store_true', help='plot and visualize the Decision Tree classifier')
data_args = parser.add_argument_group('data')
data_args.add_argument('--data_news', action='store_true', help='Training and testing on the News dataset')
data_args.add_argument('--data_mushroom', action='store_true', help='Training and testing on the Mushroom dataset')
data_args.add_argument('--data_income', action='store_true', help='Training and testing on the Income dataset')
path_args = parser.add_argument_group('train_path')
path_args.add_argument('--train_path', type=str, default='', help='training path to be specified by user')
path_args.add_argument('--train_path_news', type=str, default='../data/news/news_train.csv', help='path to the News training dataset')
path_args.add_argument('--train_path_mushroom', type=str, default='../data/mushroom/mushroom_train.csv', help='path to the Mushroom training dataset')
path_args.add_argument('--train_path_income', type=str, default='../data/income/income_train.csv', help='path to the Income training dataset')
path_args = parser.add_argument_group('test_path')
path_args.add_argument('--test_path', type=str, default='', help='testing path to be specified by user')
path_args.add_argument('--test_path_news', type=str, default='../data/news/news_test.csv', help='path to the News testing dataset')
path_args.add_argument('--test_path_mushroom', type=str, default='../data/mushroom/mushroom_test.csv', help='path to the Mushroom testing dataset')
path_args.add_argument('--test_path_income', type=str, default='../data/income/income_test.csv', help='path to the Income testing dataset')
path_args = parser.add_argument_group('output_path')
path_args.add_argument('--output_path', type=str, default='../result/output.csv', help='path to save model prediction')
args = parser.parse_args()
args = error_handling(args)
return args
##################
# ERROR HANDLING #
##################
def error_handling(args):
if args.classifier != '':
args.naive_bayes = True if args.classifier == 'N' else False
args.decision_tree = True if args.classifier == 'D' else False
if args.naive_bayes and args.decision_tree == True:
raise AssertionError('Please choose one classifier at once, or specify the correct classifier!')
if args.search_opt and args.run_all and args.visualize_tree == True:
raise AssertionError('Please choose one mode at a time!')
if args.data_news and args.data_mushroom and args.income == True:
raise AssertionError('Please choose one and at least one dataset at a time!')
if args.train_path != '' and args.test_path != '':
if not os.path.isfile(args.train_path) or not os.path.isfile(args.test_path):
raise AssertionError('The given file path is invalid!')
if args.data_news:
args.train_path_news = args.train_path
args.test_path_news = args.test_path
elif args.data_mushroom:
args.train_path_mushroom = args.train_path
args.test_path_mushroom = args.test_path
elif args.data_income:
args.train_path_income = args.train_path
args.test_path_income = args.test_path
else:
raise AssertionError('Must choose a dataset!')
return args
#################
# OUTPUT WRITER #
#################
def output_writer(path, result):
with open(path, 'w') as f:
file = csv.writer(f, delimiter=',', quotechar='\r')
for item in result:
file.writerow([int(item)])
print('Results have been successfully saved to: %s' % (path))
return True
########
# MAIN #
########
"""
main function
"""
def main():
args = get_config()
loader = data_loader(args)
#---fetch data---#
if args.data_news:
train_x, train_y, test_x, test_y = loader.fetch_news()
MODEL = 'NEWS'
elif args.data_mushroom:
train_x, train_y, test_x, test_y = loader.fetch_mushroom()
MODEL = 'MUSHROOM'
elif args.data_income:
train_x, train_y, test_x, test_y = loader.fetch_income() # -> test_y == None
MODEL = 'INCOME'
###############
# NAIVE BAYES #
###############
if args.naive_bayes:
#---construct model---#
naive_bayes = naive_bayes_runner(MODEL, train_x, train_y, test_x, test_y)
#---modes---#
if args.search_opt:
naive_bayes.search_alpha()
elif args.run_all:
naive_bayes.run_best_all()
else:
pred_y = naive_bayes.run_best()
output_writer(args.output_path, pred_y)
#################
# DECISION TREE #
#################
if args.decision_tree:
#---construct model---#
decision_tree = decision_tree_runner(MODEL, train_x, train_y, test_x, test_y)
#---modes---#
if args.search_opt:
decision_tree.search_max_depth()
elif args.visualize_tree:
decision_tree.visualize()
else:
pred_y = decision_tree.run_best()
output_writer(args.output_path, pred_y)
if __name__ == '__main__':
main()
| [((26, 10, 26, 60), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (), '', False, 'import argparse\n'), ((114, 10, 114, 27), 'data_loader.data_loader', 'data_loader', ({(114, 22, 114, 26): 'args'}, {}), '(args)', False, 'from data_loader import data_loader\n'), ((98, 9, 98, 53), 'csv.writer', 'csv.writer', (), '', False, 'import csv\n'), ((132, 16, 132, 75), 'classifiers.naive_bayes_runner', 'naive_bayes_runner', ({(132, 35, 132, 40): 'MODEL', (132, 42, 132, 49): 'train_x', (132, 51, 132, 58): 'train_y', (132, 60, 132, 66): 'test_x', (132, 68, 132, 74): 'test_y'}, {}), '(MODEL, train_x, train_y, test_x, test_y)', False, 'from classifiers import naive_bayes_runner\n'), ((148, 18, 148, 79), 'classifiers.decision_tree_runner', 'decision_tree_runner', ({(148, 39, 148, 44): 'MODEL', (148, 46, 148, 53): 'train_x', (148, 55, 148, 62): 'train_y', (148, 64, 148, 70): 'test_x', (148, 72, 148, 78): 'test_y'}, {}), '(MODEL, train_x, train_y, test_x, test_y)', False, 'from classifiers import decision_tree_runner\n'), ((77, 9, 77, 40), 'os.path.isfile', 'os.path.isfile', ({(77, 24, 77, 39): 'args.train_path'}, {}), '(args.train_path)', False, 'import os\n'), ((77, 48, 77, 78), 'os.path.isfile', 'os.path.isfile', ({(77, 63, 77, 77): 'args.test_path'}, {}), '(args.test_path)', False, 'import os\n')] |
Nick-AhSen/iGibson | igibson/metrics/agent.py | c6854f11eec5d935fa3ef3d6d4852c6571beab4b | import copy
import numpy as np
import pybullet as p
from igibson.metrics.metric_base import MetricBase
class BehaviorRobotMetric(MetricBase):
def __init__(self):
self.initialized = False
self.state_cache = {}
self.next_state_cache = {}
self.agent_pos = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.agent_grasping = {part: [] for part in ["left_hand", "right_hand"]}
self.agent_local_pos = {part: [] for part in ["left_hand", "right_hand"]}
self.agent_reset = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_work = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_distance = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_grasp_distance = {part: [] for part in ["left_hand", "right_hand"]}
self.clip = 0.2
def step_callback(self, igbhvr_act_inst, _):
robot = igbhvr_act_inst.simulator.robots[0]
agent_work = {part: 0 for part in ["left_hand", "right_hand", "body"]}
agent_distance = {part: 0 for part in ["left_hand", "right_hand", "body"]}
for part in ["left_hand", "right_hand", "body"]:
self.next_state_cache[part] = {
"position": np.array(p.getBasePositionAndOrientation(robot.parts[part].get_body_id())[0]),
}
if not self.initialized:
self.state_cache = copy.deepcopy(self.next_state_cache)
self.initialized = True
if robot.action[19] > 0 and robot.action[27] > 0:
self.agent_reset["left_hand"].append(True)
self.agent_reset["right_hand"].append(True)
self.agent_reset["body"].append(True)
if robot.action[19] > 0:
self.agent_reset["left_hand"].append(True)
self.agent_reset["right_hand"].append(False)
self.agent_reset["body"].append(True)
elif robot.action[27] > 0:
self.agent_reset["left_hand"].append(False)
self.agent_reset["right_hand"].append(True)
self.agent_reset["body"].append(True)
else:
self.agent_reset["left_hand"].append(False)
self.agent_reset["right_hand"].append(False)
self.agent_reset["body"].append(False)
for part in self.state_cache:
delta_pos = np.linalg.norm(self.next_state_cache[part]["position"] - self.state_cache[part]["position"])
self.agent_pos[part].append(list(self.state_cache[part]["position"]))
# Exclude agent teleports
delta_pos = np.clip(delta_pos, -self.clip, self.clip)
if robot.parts[part].movement_cid is None:
force = 0
work = 0
else:
force = p.getConstraintState(robot.parts[part].movement_cid)
work = np.abs((delta_pos * np.linalg.norm(force)))
distance = np.abs(delta_pos)
if part in ["left_hand", "right_hand"]:
self.agent_local_pos[part].append(list(robot.parts[part].get_local_position_orientation()[0]))
if part in ["left_hand", "right_hand"] and (
len(p.getContactPoints(robot.parts[part].get_body_id())) > 0
or robot.parts[part].object_in_hand is not None
):
self.delta_agent_grasp_distance[part].append(distance)
self.agent_grasping[part].append(True)
elif part in ["left_hand", "right_hand"]:
self.delta_agent_grasp_distance[part].append(0)
self.agent_grasping[part].append(False)
agent_work[part] = work
agent_distance[part] = distance
self.delta_agent_work[part].append(work)
self.delta_agent_distance[part].append(distance)
self.state_cache = copy.deepcopy(self.next_state_cache)
def gather_results(self):
return {
"agent_distance": {
"timestep": self.delta_agent_distance,
},
"grasp_distance": {
"timestep": self.delta_agent_grasp_distance,
},
"work": {
"timestep": self.delta_agent_work,
},
"pos": {
"timestep": self.agent_pos,
},
"local_pos": {
"timestep": self.agent_local_pos,
},
"grasping": {
"timestep": self.agent_grasping,
},
"reset": {
"timestep": self.agent_reset,
},
}
class FetchRobotMetric(MetricBase):
def __init__(self):
self.initialized = False
self.state_cache = {}
self.next_state_cache = {}
self.agent_pos = {part: [] for part in ["gripper", "body"]}
self.agent_grasping = {part: [] for part in ["gripper"]}
self.agent_local_pos = {part: [] for part in ["gripper"]}
self.delta_agent_distance = {part: [] for part in ["gripper", "body"]}
self.delta_agent_grasp_distance = {part: [] for part in ["gripper"]}
self.clip = 0.2
def step_callback(self, igbhvr_act_inst, _):
robot = igbhvr_act_inst.simulator.robots[0]
agent_distance = {part: 0 for part in self.agent_pos}
self.next_state_cache = {
"gripper": {"position": robot.get_end_effector_position()},
"body": {"position": robot.get_position()},
}
if not self.initialized:
self.state_cache = copy.deepcopy(self.next_state_cache)
self.initialized = True
self.agent_pos["body"].append(list(self.state_cache["body"]["position"]))
delta_pos = np.linalg.norm(
np.array(self.next_state_cache["body"]["position"]) - self.state_cache["body"]["position"]
)
distance = np.abs(delta_pos)
self.delta_agent_distance["body"].append(distance)
self.agent_pos["gripper"].append(list(self.state_cache["gripper"]["position"]))
delta_pos = np.linalg.norm(
self.next_state_cache["gripper"]["position"] - self.state_cache["gripper"]["position"]
)
gripper_distance = np.abs(delta_pos)
self.delta_agent_distance["gripper"].append(gripper_distance)
self.agent_local_pos["gripper"].append(list(robot.get_relative_eef_position()))
contacts = p.getContactPoints(bodyA=robot.robot_ids[0], linkIndexA=robot.eef_link_id)
if len(contacts) > 0:
self.delta_agent_grasp_distance["gripper"].append(gripper_distance)
self.agent_grasping["gripper"].append(True)
else:
self.delta_agent_grasp_distance["gripper"].append(0)
self.agent_grasping["gripper"].append(False)
self.state_cache = copy.deepcopy(self.next_state_cache)
def gather_results(self):
return {
"agent_distance": {
"timestep": self.delta_agent_distance,
},
"grasp_distance": {
"timestep": self.delta_agent_grasp_distance,
},
"pos": {
"timestep": self.agent_pos,
},
"local_pos": {
"timestep": self.agent_local_pos,
},
"grasping": {
"timestep": self.agent_grasping,
},
}
| [((91, 27, 91, 63), 'copy.deepcopy', 'copy.deepcopy', ({(91, 41, 91, 62): 'self.next_state_cache'}, {}), '(self.next_state_cache)', False, 'import copy\n'), ((153, 19, 153, 36), 'numpy.abs', 'np.abs', ({(153, 26, 153, 35): 'delta_pos'}, {}), '(delta_pos)', True, 'import numpy as np\n'), ((157, 20, 159, 9), 'numpy.linalg.norm', 'np.linalg.norm', ({(158, 12, 158, 98): "self.next_state_cache['gripper']['position'] - self.state_cache['gripper'][\n 'position']"}, {}), "(self.next_state_cache['gripper']['position'] - self.\n state_cache['gripper']['position'])", True, 'import numpy as np\n'), ((160, 27, 160, 44), 'numpy.abs', 'np.abs', ({(160, 34, 160, 43): 'delta_pos'}, {}), '(delta_pos)', True, 'import numpy as np\n'), ((165, 19, 165, 93), 'pybullet.getContactPoints', 'p.getContactPoints', (), '', True, 'import pybullet as p\n'), ((173, 27, 173, 63), 'copy.deepcopy', 'copy.deepcopy', ({(173, 41, 173, 62): 'self.next_state_cache'}, {}), '(self.next_state_cache)', False, 'import copy\n'), ((40, 31, 40, 67), 'copy.deepcopy', 'copy.deepcopy', ({(40, 45, 40, 66): 'self.next_state_cache'}, {}), '(self.next_state_cache)', False, 'import copy\n'), ((61, 24, 61, 116), 'numpy.linalg.norm', 'np.linalg.norm', ({(61, 39, 61, 115): "self.next_state_cache[part]['position'] - self.state_cache[part]['position']"}, {}), "(self.next_state_cache[part]['position'] - self.state_cache[\n part]['position'])", True, 'import numpy as np\n'), ((64, 24, 64, 65), 'numpy.clip', 'np.clip', ({(64, 32, 64, 41): 'delta_pos', (64, 43, 64, 53): '-self.clip', (64, 55, 64, 64): 'self.clip'}, {}), '(delta_pos, -self.clip, self.clip)', True, 'import numpy as np\n'), ((72, 23, 72, 40), 'numpy.abs', 'np.abs', ({(72, 30, 72, 39): 'delta_pos'}, {}), '(delta_pos)', True, 'import numpy as np\n'), ((146, 31, 146, 67), 'copy.deepcopy', 'copy.deepcopy', ({(146, 45, 146, 66): 'self.next_state_cache'}, {}), '(self.next_state_cache)', False, 'import copy\n'), ((69, 24, 69, 76), 'pybullet.getConstraintState', 'p.getConstraintState', ({(69, 45, 69, 75): 'robot.parts[part].movement_cid'}, {}), '(robot.parts[part].movement_cid)', True, 'import pybullet as p\n'), ((151, 12, 151, 63), 'numpy.array', 'np.array', ({(151, 21, 151, 62): "self.next_state_cache['body']['position']"}, {}), "(self.next_state_cache['body']['position'])", True, 'import numpy as np\n'), ((70, 43, 70, 64), 'numpy.linalg.norm', 'np.linalg.norm', ({(70, 58, 70, 63): 'force'}, {}), '(force)', True, 'import numpy as np\n')] |
Arahabica/font-subset-css | fontslice/__init__.py | 393b9a452af49c2168c7a9f84983e4170937ea67 | import sys
from .main import (
_chunk_list,
_get_unicode_range_hash,
convert_unicode_range,
get_120_unicode_ranges,
get_unicode_ranges_from_text,
generate_css,
main,
)
__all__ = [
"_chunk_list",
"_get_unicode_range_hash",
"convert_unicode_range",
"get_120_unicode_ranges",
"get_unicode_ranges_from_text",
"generate_css",
"main",
]
if __name__ == "__main__":
sys.exit(main())
| [] |
MrJaatt/ttkbootstrap | src/ttkbootstrap/dialogs/dialogs.py | 4e837d64859e5a230ef0500faddbb2c384f5b9d4 | """
This module contains various base dialog base classes that can be
used to create custom dialogs for the end user.
These classes serve as the basis for the pre-defined static helper
methods in the `Messagebox`, and `Querybox` container classes.
"""
import calendar
import textwrap
from datetime import datetime
from tkinter import font
import ttkbootstrap as ttk
from ttkbootstrap import utility
from ttkbootstrap.icons import Icon
from ttkbootstrap.constants import *
from tkinter import BaseWidget
from ttkbootstrap.localization import MessageCatalog
class Dialog(BaseWidget):
"""A simple dialog base class."""
def __init__(self, parent=None, title="", alert=False):
"""
Parameters:
parent (Widget):
Makes the window the logical parent of the message box.
The messagebox is displayed on top of its parent window.
title (str):
The string displayed as the title of the message box.
This option is ignored on Mac OS X, where platform
guidelines forbid the use of a title on this kind of
dialog.
alert (bool):
Ring the display's bell when the dialog is shown.
"""
BaseWidget._setup(self, parent, {})
self._winsys = self.master.tk.call("tk", "windowingsystem")
self._toplevel = None
self._title = title or " "
self._result = None
self._alert = alert
self._initial_focus = None
def _locate(self):
toplevel = self._toplevel
master = toplevel.master
screen_height = toplevel.winfo_screenheight()
screen_width = toplevel.winfo_screenwidth()
toplevel.update_idletasks()
if master.winfo_viewable():
m_width = master.winfo_width()
m_height = master.winfo_height()
m_x = master.winfo_rootx()
m_y = master.winfo_rooty()
else:
m_width = screen_width
m_height = screen_height
m_x = m_y = 0
w_width = toplevel.winfo_reqwidth()
w_height = toplevel.winfo_reqheight()
x = int(m_x + (m_width - w_width) * 0.45)
y = int(m_y + (m_height - w_height) * 0.3)
if x + w_width > screen_width:
x = screen_width - w_width
elif x < 0:
x = 0
if y + w_height > screen_height:
y = screen_height - w_height
elif y < 0:
y = 0
toplevel.geometry(f"+{x}+{y}")
def show(self):
"""Show the popup dialog"""
self._result = None
self.build()
self._locate()
self._toplevel.deiconify()
if self._alert:
self._toplevel.bell()
if self._initial_focus:
self._initial_focus.focus_force()
self._toplevel.grab_set()
self._toplevel.wait_window()
def create_body(self, master):
"""Create the dialog body.
This method should be overridden and is called by the `build`
method. Set the `self._initial_focus` for the widget that
should receive the initial focus.
Parameters:
master (Widget):
The parent widget.
"""
raise NotImplementedError
def create_buttonbox(self, master):
"""Create the dialog button box.
This method should be overridden and is called by the `build`
method. Set the `self._initial_focus` for the button that
should receive the intial focus.
Parameters:
master (Widget):
The parent widget.
"""
raise NotImplementedError
def build(self):
"""Build the dialog from settings"""
# setup toplevel based on widowing system
if self._winsys == "win32":
self._toplevel = ttk.Toplevel(
transient=self.master,
title=self._title,
resizable=(0, 0),
minsize=(250, 15),
iconify=True,
)
else:
self._toplevel = ttk.Toplevel(
transient=self.master,
title=self._title,
resizable=(0, 0),
windowtype="dialog",
iconify=True,
)
self._toplevel.withdraw() # reset the iconify state
# bind <Escape> event to window close
self._toplevel.bind("<Escape>", lambda _: self._toplevel.destroy())
# set position of popup from parent window
#self._locate()
# create widgets
self.create_body(self._toplevel)
self.create_buttonbox(self._toplevel)
# update the window before showing
self._toplevel.update_idletasks()
@property
def result(self):
"""Returns the result of the dialog."""
return self._result
class MessageDialog(Dialog):
"""A simple modal dialog class that can be used to build simple
message dialogs.
Displays a message and a set of buttons. Each of the buttons in the
message window is identified by a unique symbolic name. After the
message window is popped up, the message box awaits for the user to
select one of the buttons. Then it returns the symbolic name of the
selected button. Use a `Toplevel` widget for more advanced modal
dialog designs.
"""
def __init__(
self,
message,
title=" ",
buttons=None,
command=None,
width=50,
parent=None,
alert=False,
default=None,
padding=(20, 20),
icon=None,
**kwargs
):
"""
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the message box.
This option is ignored on Mac OS X, where platform
guidelines forbid the use of a title on this kind of
dialog.
buttons (List[str]):
A list of buttons to appear at the bottom of the popup
messagebox. The buttons can be a list of strings which
will define the symbolic name and the button text.
`['OK', 'Cancel']`. Alternatively, you can assign a
bootstyle to each button by using the colon to separate the
button text and the bootstyle. If no colon is found, then
the style is set to 'primary' by default.
`['OK:success','Cancel:danger']`.
command (Tuple[Callable, str]):
The function to invoke when the user closes the dialog.
The actual command is a tuple that consists of the
function to call and the symbolic name of the button that
closes the dialog.
width (int):
The maximum number of characters per line in the message.
If the text stretches beyond the limit, the line will break
at the word.
parent (Widget):
Makes the window the logical parent of the message box.
The messagebox is displayed on top of its parent window.
alert (bool):
Ring the display's bell when the dialog is shown.
default (str):
The symbolic name of the default button. The default
button is invoked when the the <Return> key is pressed.
If no default is provided, the right-most button in the
button list will be set as the default.,
padding (Union[int, Tuple[int]]):
The amount of space between the border and the widget
contents.
icon (str):
An image path, path-like object or image data to be
displayed to the left of the text.
**kwargs (Dict):
Other optional keyword arguments.
Example:
```python
root = tk.Tk()
md = MessageDialog("Displays a message with buttons.")
md.show()
```
"""
super().__init__(parent, title, alert)
self._message = message
self._command = command
self._width = width
self._alert = alert
self._default = (default,)
self._padding = padding
self._icon = icon
self._localize = kwargs.get('localize')
if buttons is None:
self._buttons = [
f"{MessageCatalog.translate('Cancel')}:secondary",
f"{MessageCatalog.translate('OK')}:primary"
]
else:
self._buttons = buttons
def create_body(self, master):
"""Overrides the parent method; adds the message section."""
container = ttk.Frame(master, padding=self._padding)
if self._icon:
try:
# assume this is image data
self._img = ttk.PhotoImage(data=self._icon)
icon_lbl = ttk.Label(container, image=self._img)
icon_lbl.pack(side=LEFT, padx=5)
except:
try:
# assume this is a file path
self._img = ttk.PhotoImage(file=self._icon)
icon_lbl = ttk.Label(container, image=self._img)
icon_lbl.pack(side=LEFT, padx=5)
except:
# icon is neither data nor a valid file path
print('MessageDialog icon is invalid')
if self._message:
for msg in self._message.split("\n"):
message = "\n".join(textwrap.wrap(msg, width=self._width))
message_label = ttk.Label(container, text=message)
message_label.pack(pady=(0, 3), fill=X, anchor=N)
container.pack(fill=X, expand=True)
def create_buttonbox(self, master):
"""Overrides the parent method; adds the message buttonbox"""
frame = ttk.Frame(master, padding=(5, 5))
button_list = []
for i, button in enumerate(self._buttons[::-1]):
cnf = button.split(":")
if len(cnf) == 2:
text, bootstyle = cnf
else:
text = cnf[0]
bootstyle = "secondary"
if self._localize == True:
text = MessageCatalog.translate(text)
btn = ttk.Button(frame, bootstyle=bootstyle, text=text)
btn.bind("<Return>", lambda _: btn.invoke())
btn.configure(command=lambda b=btn: self.on_button_press(b))
btn.pack(padx=2, side=RIGHT)
btn.lower() # set focus traversal left-to-right
button_list.append(btn)
if self._default is not None and text == self._default:
self._initial_focus = btn
elif self._default is None and i == 0:
self._initial_focus = btn
# bind default button to return key press and set focus
self._toplevel.bind("<Return>", lambda _, b=btn: b.invoke())
self._toplevel.bind("<KP_Enter>", lambda _, b=btn: b.invoke())
ttk.Separator(self._toplevel).pack(fill=X)
frame.pack(side=BOTTOM, fill=X, anchor=S)
if not self._initial_focus:
self._initial_focus = button_list[0]
def on_button_press(self, button):
"""Save result, destroy the toplevel, and execute command."""
self._result = button["text"]
command = self._command
if command is not None:
command()
self._toplevel.destroy()
def show(self):
"""Create and display the popup messagebox."""
super().show()
class QueryDialog(Dialog):
"""A simple modal dialog class that can be used to build simple
data input dialogs. Displays a prompt, and input box, and a set of
buttons. Additional data manipulation can be performed on the
user input post-hoc by overriding the `apply` method.
Use a `Toplevel` widget for more advanced modal dialog designs.
"""
def __init__(
self,
prompt,
title=" ",
initialvalue="",
minvalue=None,
maxvalue=None,
width=65,
datatype=str,
padding=(20, 20),
parent=None,
):
"""
Parameters:
prompt (str):
A message to display in the message box above the entry
widget.
title (str):
The string displayed as the title of the message box.
This option is ignored on Mac OS X, where platform
guidelines forbid the use of a title on this kind of
dialog.
initialvalue (Any):
The initial value in the entry widget.
minvalue (Any):
The minimum allowed value. Only valid for int and float
data types.
maxvalue (Any):
The maximum allowed value. Only valid for int and float
data types.
width (int):
The maximum number of characters per line in the
message. If the text stretches beyond the limit, the
line will break at the word.
parent (Widget):
Makes the window the logical parent of the message box.
The messagebox is displayed on top of its parent
window.
padding (Union[int, Tuple[int]]):
The amount of space between the border and the widget
contents.
datatype (Union[int, str, float]):
The data type used to validate the entry value.
"""
super().__init__(parent, title)
self._prompt = prompt
self._initialvalue = initialvalue
self._minvalue = minvalue
self._maxvalue = maxvalue
self._width = width
self._datatype = datatype
self._padding = padding
self._result = None
def create_body(self, master):
"""Overrides the parent method; adds the message and input
section."""
frame = ttk.Frame(master, padding=self._padding)
if self._prompt:
for p in self._prompt.split("\n"):
prompt = "\n".join(textwrap.wrap(p, width=self._width))
prompt_label = ttk.Label(frame, text=prompt)
prompt_label.pack(pady=(0, 5), fill=X, anchor=N)
entry = ttk.Entry(master=frame)
entry.insert(END, self._initialvalue)
entry.pack(pady=(0, 5), fill=X)
entry.bind("<Return>", self.on_submit)
entry.bind("<KP_Enter>", self.on_submit)
entry.bind("<Escape>", self.on_cancel)
frame.pack(fill=X, expand=True)
self._initial_focus = entry
def create_buttonbox(self, master):
"""Overrides the parent method; adds the message buttonbox"""
frame = ttk.Frame(master, padding=(5, 10))
submit = ttk.Button(
master=frame,
bootstyle="primary",
text=MessageCatalog.translate("Submit"),
command=self.on_submit,
)
submit.pack(padx=5, side=RIGHT)
submit.lower() # set focus traversal left-to-right
cancel = ttk.Button(
master=frame,
bootstyle="secondary",
text=MessageCatalog.translate("Cancel"),
command=self.on_cancel,
)
cancel.pack(padx=5, side=RIGHT)
cancel.lower() # set focus traversal left-to-right
ttk.Separator(self._toplevel).pack(fill=X)
frame.pack(side=BOTTOM, fill=X, anchor=S)
def on_submit(self, *_):
"""Save result, destroy the toplevel, and apply any post-hoc
data manipulations."""
self._result = self._initial_focus.get()
valid_result = self.validate()
if not valid_result:
return # keep toplevel open for valid response
self._toplevel.destroy()
self.apply()
def on_cancel(self, *_):
"""Close the toplevel and return empty."""
self._toplevel.destroy()
return
def validate(self):
"""Validate the data
This method is called automatically to validate the data before
the dialog is destroyed. Can be subclassed and overridden.
"""
# no default checks required for string data types
if self._datatype not in [float, int, complex]:
return True
# convert result to appropriate data type
try:
self._result = self._datatype(self._result)
except ValueError:
msg = MessageCatalog.translate('Should be of data type')
Messagebox.ok(
message=f"{msg} `{self._datatype}`",
title=MessageCatalog.translate("Invalid data type"),
)
return False
# max value range
if self._maxvalue is not None:
if self._result > self._maxvalue:
msg = MessageCatalog.translate('Number cannot be greater than')
Messagebox.ok(
message=f"{msg} {self._maxvalue}",
title=MessageCatalog.translate("Out of range"),
)
return False
# min value range
if self._minvalue is not None:
if self._result < self._minvalue:
msg = MessageCatalog.translate('Number cannot be less than')
Messagebox.ok(
message=f"{msg} {self._minvalue}",
title=MessageCatalog.translate("Out of range"),
)
return False
# valid result
return True
def apply(self):
"""Process the data.
This method is called automatically to process the data after
the dialog is destroyed. By default, it does nothing.
"""
pass # override
class DatePickerDialog:
"""A dialog that displays a calendar popup and returns the
selected date as a datetime object.
The current date is displayed by default unless the `startdate`
parameter is provided.
The month can be changed by clicking the chevrons to the left
and right of the month-year title.
Left-click the arrow to move the calendar by one month.
Right-click the arrow to move the calendar by one year.
Right-click the title to reset the calendar to the start date.
The starting weekday can be changed with the `firstweekday`
parameter for geographies that do not start the calendar on
Sunday, which is the default.
The widget grabs focus and all screen events until released.
If you want to cancel a date selection, click the 'X' button
at the top-right corner of the widget.
The bootstyle api may be used to change the style of the widget.
The available colors include -> primary, secondary, success,
info, warning, danger, light, dark.
"""
def __init__(
self,
parent=None,
title=" ",
firstweekday=6,
startdate=None,
bootstyle=PRIMARY,
):
"""
Parameters:
parent (Widget):
The parent widget; the popup will appear to the
bottom-right of the parent widget. If no parent is
provided, the widget is centered on the screen.
title (str):
The text that appears on the titlebar.
firstweekday (int):
Specifies the first day of the week. 0=Monday,
1=Tuesday, etc...
startdate (datetime):
The date to be in focus when the widget is
displayed.
bootstyle (str):
The following colors can be used to change the color of
the title and hover / pressed color -> primary,
secondary, info, warning, success, danger, light, dark.
"""
self.parent = parent
self.root = ttk.Toplevel(
title=title,
transient=self.parent,
resizable=(False, False),
topmost=True,
minsize=(226, 1),
iconify=True
)
self.firstweekday = firstweekday
self.startdate = startdate or datetime.today().date()
self.bootstyle = bootstyle or PRIMARY
self.date_selected = self.startdate
self.date = startdate or self.date_selected
self.calendar = calendar.Calendar(firstweekday=firstweekday)
self.titlevar = ttk.StringVar()
self.datevar = ttk.IntVar()
self._setup_calendar()
self.root.grab_set()
self.root.wait_window()
def _setup_calendar(self):
"""Setup the calendar widget"""
# create the widget containers
self.frm_calendar = ttk.Frame(
master=self.root, padding=0, borderwidth=0, relief=FLAT
)
self.frm_calendar.pack(fill=BOTH, expand=YES)
self.frm_title = ttk.Frame(self.frm_calendar, padding=(3, 3))
self.frm_title.pack(fill=X)
self.frm_header = ttk.Frame(self.frm_calendar, bootstyle=SECONDARY)
self.frm_header.pack(fill=X)
# setup the toplevel widget
self.root.withdraw() # reset the iconify state
self.frm_calendar.update_idletasks() # actualize geometry
# create visual components
self._draw_titlebar()
self._draw_calendar()
# make toplevel visible
self._set_window_position()
self.root.deiconify()
def _update_widget_bootstyle(self):
self.frm_title.configure(bootstyle=self.bootstyle)
self.title.configure(bootstyle=f"{self.bootstyle}-inverse")
self.prev_period.configure(style=f"Chevron.{self.bootstyle}.TButton")
self.next_period.configure(style=f"Chevron.{self.bootstyle}.TButton")
def _draw_calendar(self):
self._update_widget_bootstyle()
self._set_title()
self._current_month_days()
self.frm_dates = ttk.Frame(self.frm_calendar)
self.frm_dates.pack(fill=BOTH, expand=YES)
for row, weekday_list in enumerate(self.monthdays):
for col, day in enumerate(weekday_list):
self.frm_dates.columnconfigure(col, weight=1)
if day == 0:
ttk.Label(
master=self.frm_dates,
text=self.monthdates[row][col].day,
anchor=CENTER,
padding=5,
bootstyle=SECONDARY,
).grid(row=row, column=col, sticky=NSEW)
else:
if all(
[
day == self.date_selected.day,
self.date.month == self.date_selected.month,
self.date.year == self.date_selected.year,
]
):
day_style = "secondary-toolbutton"
else:
day_style = f"{self.bootstyle}-calendar"
def selected(x=row, y=col):
self._on_date_selected(x, y)
btn = ttk.Radiobutton(
master=self.frm_dates,
variable=self.datevar,
value=day,
text=day,
bootstyle=day_style,
padding=5,
command=selected,
)
btn.grid(row=row, column=col, sticky=NSEW)
def _draw_titlebar(self):
"""Draw the calendar title bar which includes the month title
and the buttons that increment and decrement the selected
month.
In addition to the previous and next MONTH commands that are
assigned to the button press, a "right-click" event is assigned
to each button that causes the calendar to move to the previous
and next YEAR.
"""
# create and pack the title and action buttons
self.prev_period = ttk.Button(
master=self.frm_title, text="«", command=self.on_prev_month
)
self.prev_period.pack(side=LEFT)
self.title = ttk.Label(
master=self.frm_title,
textvariable=self.titlevar,
anchor=CENTER,
font="-weight bold",
)
self.title.pack(side=LEFT, fill=X, expand=YES)
self.next_period = ttk.Button(
master=self.frm_title,
text="»",
command=self.on_next_month,
)
self.next_period.pack(side=LEFT)
# bind "year" callbacks to action buttons
self.prev_period.bind("<Button-3>", self.on_prev_year, "+")
self.next_period.bind("<Button-3>", self.on_next_year, "+")
self.title.bind("<Button-1>", self.on_reset_date)
# create and pack days of the week header
for col in self._header_columns():
ttk.Label(
master=self.frm_header,
text=col,
anchor=CENTER,
padding=5,
bootstyle=(SECONDARY, INVERSE),
).pack(side=LEFT, fill=X, expand=YES)
def _set_title(self):
_titledate = f'{self.date.strftime("%B %Y")}'
self.titlevar.set(value=_titledate)
def _current_month_days(self):
"""Fetch the day numbers and dates for all days in the current
month. `monthdays` is a list of days as integers, and
`monthdates` is a list of `datetime` objects.
"""
self.monthdays = self.calendar.monthdayscalendar(
year=self.date.year, month=self.date.month
)
self.monthdates = self.calendar.monthdatescalendar(
year=self.date.year, month=self.date.month
)
def _header_columns(self):
"""Create and return a list of weekdays to be used as a header
in the calendar. The order of the weekdays is based on the
`firstweekday` property.
Returns:
List[str]:
A list of weekday column names for the calendar header.
"""
weekdays = [MessageCatalog.translate("Mo"),
MessageCatalog.translate("Tu"),
MessageCatalog.translate("We"),
MessageCatalog.translate("Th"),
MessageCatalog.translate("Fr"),
MessageCatalog.translate("Sa"),
MessageCatalog.translate("Su")]
header = weekdays[self.firstweekday :] + weekdays[: self.firstweekday]
return header
def _on_date_selected(self, row, col):
"""Callback for selecting a date.
An index is assigned to each date button that corresponds to
the dates in the `monthdates` matrix. When the user clicks a
button to select a date, the index from this button is used
to lookup the date value of the button based on the row and
column index reference. This value is saved in the
`date_selected` property and the `Toplevel` is destroyed.
Parameters:
index (Tuple[int, int]):
A row and column index of the date selected; to be
found in the `monthdates` matrix.
Returns:
datetime:
The date selected
"""
self.date_selected = self.monthdates[row][col]
self.root.destroy()
def _selection_callback(func):
"""Calls the decorated `func` and redraws the calendar."""
def inner(self, *args):
func(self, *args)
self.frm_dates.destroy()
self._draw_calendar()
return inner
@_selection_callback
def on_next_month(self):
"""Increment the calendar data to the next month"""
year, month = self._nextmonth(self.date.year, self.date.month)
self.date = datetime(year=year, month=month, day=1).date()
@_selection_callback
def on_next_year(self, *_):
"""Increment the calendar data to the next year"""
year = self.date.year + 1
month = self.date.month
self.date = datetime(year=year, month=month, day=1).date()
@_selection_callback
def on_prev_month(self):
"""Decrement the calendar to the previous year"""
year, month = self._prevmonth(self.date.year, self.date.month)
self.date = datetime(year=year, month=month, day=1).date()
@_selection_callback
def on_prev_year(self, *_):
year = self.date.year - 1
month = self.date.month
self.date = datetime(year=year, month=month, day=1).date()
@_selection_callback
def on_reset_date(self, *_):
"""Set the calendar to the start date"""
self.date = self.startdate
def _set_window_position(self):
"""Move the window the to bottom-right of the parent widget, or
to the middle of the screen if no parent is provided.
"""
width = self.root.winfo_reqwidth()
height = self.root.winfo_reqheight()
if self.parent:
xpos = self.parent.winfo_rootx() + self.parent.winfo_width()
ypos = self.parent.winfo_rooty() + self.parent.winfo_height()
self.root.geometry(f"+{xpos}+{ypos}")
else:
xpos = self.root.winfo_screenwidth() // 2 - width
ypos = self.root.winfo_screenheight() // 2 - height
self.root.geometry(f"+{xpos}+{ypos}")
@staticmethod
def _nextmonth(year, month):
if month == 12:
return year+1, 1
else:
return year, month+1
@staticmethod
def _prevmonth(year, month):
if month == 1:
return year-1, 12
else:
return year, month-1
class FontDialog(Dialog):
"""A dialog that displays a variety of options for choosing a font.
This dialog constructs and returns a `Font` object based on the
options selected by the user. The initial font is based on OS
settings and will vary.
The font object is returned when the **Ok** button is pressed and
can be passed to any widget that accepts a _font_ configuration
option.
"""
def __init__(self, title="Font Selector", parent=None):
title = MessageCatalog.translate(title)
super().__init__(parent=parent, title=title)
self._style = ttk.Style()
self._default = font.nametofont("TkDefaultFont")
self._actual = self._default.actual()
self._size = ttk.Variable(value=self._actual["size"])
self._family = ttk.Variable(value=self._actual["family"])
self._slant = ttk.Variable(value=self._actual["slant"])
self._weight = ttk.Variable(value=self._actual["weight"])
self._overstrike = ttk.Variable(value=self._actual["overstrike"])
self._underline = ttk.Variable(value=self._actual["underline"])
self._preview_font = font.Font()
self._slant.trace_add("write", self._update_font_preview)
self._weight.trace_add("write", self._update_font_preview)
self._overstrike.trace_add("write", self._update_font_preview)
self._underline.trace_add("write", self._update_font_preview)
_headingfont = font.nametofont("TkHeadingFont")
_headingfont.configure(weight="bold")
self._update_font_preview()
self._families = set([self._family.get()])
for f in font.families():
if all([f, not f.startswith("@"), "emoji" not in f.lower()]):
self._families.add(f)
def create_body(self, master):
width = utility.scale_size(master, 600)
height = utility.scale_size(master, 500)
self._toplevel.geometry(f"{width}x{height}")
family_size_frame = ttk.Frame(master, padding=10)
family_size_frame.pack(fill=X, anchor=N)
self._initial_focus = self._font_families_selector(family_size_frame)
self._font_size_selector(family_size_frame)
self._font_options_selectors(master, padding=10)
self._font_preview(master, padding=10)
def create_buttonbox(self, master):
container = ttk.Frame(master, padding=(5, 10))
container.pack(fill=X)
ok_btn = ttk.Button(
master=container,
bootstyle="primary",
text=MessageCatalog.translate("OK"),
command=self._on_submit,
)
ok_btn.pack(side=RIGHT, padx=5)
ok_btn.bind("<Return>", lambda _: ok_btn.invoke())
cancel_btn = ttk.Button(
master=container,
bootstyle="secondary",
text=MessageCatalog.translate("Cancel"),
command=self._on_cancel,
)
cancel_btn.pack(side=RIGHT, padx=5)
cancel_btn.bind("<Return>", lambda _: cancel_btn.invoke())
def _font_families_selector(self, master):
container = ttk.Frame(master)
container.pack(fill=BOTH, expand=YES, side=LEFT)
header = ttk.Label(container, text=MessageCatalog.translate("Family"), font="TkHeadingFont")
header.pack(fill=X, pady=(0, 2), anchor=N)
listbox = ttk.Treeview(
master=container,
height=5,
show="",
columns=[0],
)
listbox.column(0, width=utility.scale_size(listbox, 250))
listbox.pack(side=LEFT, fill=BOTH, expand=YES)
listbox_vbar = ttk.Scrollbar(
container,
command=listbox.yview,
orient=VERTICAL,
bootstyle="rounded",
)
listbox_vbar.pack(side=RIGHT, fill=Y)
listbox.configure(yscrollcommand=listbox_vbar.set)
for f in self._families:
listbox.insert("", iid=f, index=END, tags=[f], values=[f])
listbox.tag_configure(f, font=(f, self._size.get()))
iid = self._family.get()
listbox.selection_set(iid) # select default value
listbox.see(iid) # ensure default is visible
listbox.bind(
"<<TreeviewSelect>>", lambda e: self._on_select_font_family(e)
)
return listbox
def _font_size_selector(self, master):
container = ttk.Frame(master)
container.pack(side=LEFT, fill=Y, padx=(10, 0))
header = ttk.Label(container, text=MessageCatalog.translate("Size"), font="TkHeadingFont")
header.pack(fill=X, pady=(0, 2), anchor=N)
sizes_listbox = ttk.Treeview(container, height=7, columns=[0], show="")
sizes_listbox.column(0, width=utility.scale_size(sizes_listbox, 24))
sizes = [*range(8, 13), *range(13, 30, 2), 36, 48, 72]
for s in sizes:
sizes_listbox.insert("", iid=s, index=END, values=[s])
iid = self._size.get()
sizes_listbox.selection_set(iid)
sizes_listbox.see(iid)
sizes_listbox.bind(
"<<TreeviewSelect>>", lambda e: self._on_select_font_size(e)
)
sizes_listbox_vbar = ttk.Scrollbar(
master=container,
orient=VERTICAL,
command=sizes_listbox.yview,
bootstyle="round",
)
sizes_listbox.configure(yscrollcommand=sizes_listbox_vbar.set)
sizes_listbox.pack(side=LEFT, fill=Y, expand=YES, anchor=N)
sizes_listbox_vbar.pack(side=LEFT, fill=Y, expand=YES)
def _font_options_selectors(self, master, padding: int):
container = ttk.Frame(master, padding=padding)
container.pack(fill=X, padx=2, pady=2, anchor=N)
weight_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Weight"), padding=5)
weight_lframe.pack(side=LEFT, fill=X, expand=YES)
opt_normal = ttk.Radiobutton(
master=weight_lframe,
text=MessageCatalog.translate("normal"),
value="normal",
variable=self._weight,
)
opt_normal.invoke()
opt_normal.pack(side=LEFT, padx=5, pady=5)
opt_bold = ttk.Radiobutton(
master=weight_lframe,
text=MessageCatalog.translate("bold"),
value="bold",
variable=self._weight,
)
opt_bold.pack(side=LEFT, padx=5, pady=5)
slant_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Slant"), padding=5)
slant_lframe.pack(side=LEFT, fill=X, padx=10, expand=YES)
opt_roman = ttk.Radiobutton(
master=slant_lframe,
text=MessageCatalog.translate("roman"),
value="roman",
variable=self._slant,
)
opt_roman.invoke()
opt_roman.pack(side=LEFT, padx=5, pady=5)
opt_italic = ttk.Radiobutton(
master=slant_lframe,
text=MessageCatalog.translate("italic"),
value="italic",
variable=self._slant,
)
opt_italic.pack(side=LEFT, padx=5, pady=5)
effects_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Effects"), padding=5)
effects_lframe.pack(side=LEFT, padx=(2, 0), fill=X, expand=YES)
opt_underline = ttk.Checkbutton(
master=effects_lframe, text=MessageCatalog.translate("underline"), variable=self._underline
)
opt_underline.pack(side=LEFT, padx=5, pady=5)
opt_overstrike = ttk.Checkbutton(
master=effects_lframe, text=MessageCatalog.translate("overstrike"), variable=self._overstrike
)
opt_overstrike.pack(side=LEFT, padx=5, pady=5)
def _font_preview(self, master, padding: int):
container = ttk.Frame(master, padding=padding)
container.pack(fill=BOTH, expand=YES, anchor=N)
header = ttk.Label(container, text=MessageCatalog.translate("Preview"), font="TkHeadingFont")
header.pack(fill=X, pady=2, anchor=N)
content = MessageCatalog.translate("The quick brown fox jumps over the lazy dog.")
self._preview_text = ttk.Text(
master=container,
height=3,
font=self._preview_font,
highlightbackground=self._style.colors.primary,
)
self._preview_text.insert(END, content)
self._preview_text.pack(fill=BOTH, expand=YES)
container.pack_propagate(False)
def _on_select_font_family(self, e):
tree: ttk.Treeview = self._toplevel.nametowidget(e.widget)
fontfamily = tree.selection()[0]
self._family.set(value=fontfamily)
self._update_font_preview()
def _on_select_font_size(self, e):
tree: ttk.Treeview = self._toplevel.nametowidget(e.widget)
fontsize = tree.selection()[0]
self._size.set(value=fontsize)
self._update_font_preview()
def _on_submit(self) -> font.Font:
self._toplevel.destroy()
return self.result
def _on_cancel(self):
self._toplevel.destroy()
def _update_font_preview(self, *_):
family = self._family.get()
size = self._size.get()
slant = self._slant.get()
overstrike = self._overstrike.get()
underline = self._underline.get()
self._preview_font.config(
family=family,
size=size,
slant=slant,
overstrike=overstrike,
underline=underline,
)
try:
self._preview_text.configure(font=self._preview_font)
except:
pass
self._result = self._preview_font
class Messagebox:
"""This class contains various static methods that show popups with
a message to the end user with various arrangments of buttons
and alert options."""
@staticmethod
def show_info(message, title=" ", parent=None, **kwargs):
"""Display a modal dialog box with an OK button and an INFO
icon.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
"""
sd = MessageDialog(
message=message,
title=title,
parent=parent,
buttons=["OK:primary"],
icon=Icon.info,
localize=True
)
sd.show()
@staticmethod
def show_warning(message, title=" ", parent=None, **kwargs):
"""Display a modal dialog box with an OK button and a
warning icon. Also will ring the display bell.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
"""
sd = MessageDialog(
message=message,
title=title,
parent=parent,
buttons=["OK:primary"],
icon=Icon.warning,
alert=True,
localize=True,
**kwargs,
)
sd.show()
@staticmethod
def show_error(message, title=" ", parent=None, **kwargs):
"""Display a modal dialog box with an OK button and an
error icon. Also will ring the display bell.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
"""
sd = MessageDialog(
message=message,
title=title,
parent=parent,
buttons=["OK:primary"],
icon=Icon.error,
alert=True,
localize=True,
**kwargs,
)
sd.show()
@staticmethod
def show_question(
message,
title=" ",
parent=None,
buttons=["No:secondary", "Yes:primary"],
**kwargs,
):
"""Display a modal dialog box with yes, no buttons and a
question icon. Also will ring the display bell. You may also
change the button scheme using the `buttons` parameter.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
buttons (List[str]):
A list of buttons to appear at the bottom of the popup
messagebox. The buttons can be a list of strings which
will define the symbolic name and the button text.
`['OK', 'Cancel']`. Alternatively, you can assign a
bootstyle to each button by using the colon to separate the
button text and the bootstyle. If no colon is found, then
the style is set to 'primary' by default.
`['Yes:success','No:danger']`.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
Union[str, None]:
The symbolic name of the button pressed, or None if the
window is closed without pressing a button.
"""
sd = MessageDialog(
message=message,
title=title,
parent=parent,
buttons=buttons,
icon=Icon.question,
alert=True,
localize=True,
**kwargs,
)
sd.show()
return sd.result
@staticmethod
def ok(message, title=" ", alert=False, parent=None, **kwargs):
"""Display a modal dialog box with an OK button and and optional
bell alert.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
alert (bool):
Specified whether to ring the display bell.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
"""
sd = MessageDialog(
title=title,
message=message,
parent=parent,
alert=alert,
buttons=["OK:primary"],
localize=True,
**kwargs,
)
sd.show()
@staticmethod
def okcancel(message, title=" ", alert=False, parent=None, **kwargs):
"""Displays a modal dialog box with OK and Cancel buttons and
return the symbolic name of the button pressed.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
alert (bool):
Specified whether to ring the display bell.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
Union[str, None]:
The symbolic name of the button pressed, or None if the
window is closed without pressing a button.
"""
sd = MessageDialog(
title=title, message=message, parent=parent, alert=alert, localize=True, **kwargs
)
sd.show()
return sd.result
@staticmethod
def yesno(message, title=" ", alert=False, parent=None, **kwargs):
"""Display a modal dialog box with YES and NO buttons and return
the symbolic name of the button pressed.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
alert (bool):
Specified whether to ring the display bell.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
Union[str, None]:
The symbolic name of the button pressed, or None if the
window is closed without pressing a button.
"""
sd = MessageDialog(
title=title,
message=message,
parent=parent,
buttons=["No", "Yes:primary"],
alert=alert,
localize=True,
**kwargs,
)
sd.show()
return sd.result
@staticmethod
def yesnocancel(message, title=" ", alert=False, parent=None, **kwargs):
"""Display a modal dialog box with YES, NO, and Cancel buttons,
and return the symbolic name of the button pressed.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
alert (bool):
Specified whether to ring the display bell.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Optional keyword arguments.
Returns:
Union[str, None]:
The symbolic name of the button pressed, or None if the
window is closed without pressing a button.
"""
sd = MessageDialog(
title=title,
message=message,
parent=parent,
alert=alert,
buttons=["Cancel", "No", "Yes:primary"],
localize=True,
**kwargs,
)
sd.show()
return sd.result
@staticmethod
def retrycancel(message, title=" ", alert=False, parent=None, **kwargs):
"""Display a modal dialog box with RETRY and Cancel buttons;
returns the symbolic name of the button pressed.
Parameters:
message (str):
A message to display in the message box.
title (str):
The string displayed as the title of the messagebox. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
alert (bool):
Specified whether to ring the display bell.
parent (Union[Window, Toplevel]):
Makes the window the logical parent of the message box. The
message box is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
Union[str, None]:
The symbolic name of the button pressed, or None if the
window is closed without pressing a button.
"""
sd = MessageDialog(
title=title,
message=message,
parent=parent,
alert=alert,
buttons=["Cancel", "Retry:primary"],
localize=True,
**kwargs,
)
sd.show()
return sd.result
class Querybox:
"""This class contains various static methods that request data
from the end user."""
@staticmethod
def get_color(
parent=None,
title="Color Chooser",
initialcolor=None,
):
"""Show a color picker and return the select color when the
user pressed OK.
Parameters:
parent (Widget):
The parent widget.
title (str):
Optional text that appears on the titlebar.
initialcolor (str):
The initial color to display in the 'Current' color
frame.
Returns:
Tuple[rgb, hsl, hex]
The selected color in various colors models.
"""
from ttkbootstrap.dialogs.colorchooser import ColorChooserDialog
cd = ColorChooserDialog(parent, title, initialcolor)
cd.show()
return cd.result
@staticmethod
def get_date(
parent=None,
title=" ",
firstweekday=6,
startdate=None,
bootstyle="primary",
):
"""Shows a calendar popup and returns the selection.
Parameters:
parent (Widget):
The parent widget; the popup will appear to the
bottom-right of the parent widget. If no parent is
provided, the widget is centered on the screen.
title (str):
The text that appears on the popup titlebar.
firstweekday (int):
Specifies the first day of the week. `0` is Monday, `6` is
Sunday (the default).
startdate (datetime):
The date to be in focus when the widget is displayed;
bootstyle (str):
The following colors can be used to change the color of the
title and hover / pressed color -> primary, secondary, info,
warning, success, danger, light, dark.
Returns:
datetime:
The date selected; the current date if no date is selected.
"""
chooser = DatePickerDialog(
parent=parent,
title=title,
firstweekday=firstweekday,
startdate=startdate,
bootstyle=bootstyle,
)
return chooser.date_selected
@staticmethod
def get_string(
prompt="", title=" ", initialvalue=None, parent=None, **kwargs
):
"""Request a string type input from the user.
Parameters:
prompt (str):
A message to display in the message box above the entry
widget.
title (str):
The string displayed as the title of the message box. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
initialvalue (Any):
The initial value in the entry widget.
parent (Widget):
Makes the window the logical parent of the message box. The
messagebox is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
str:
The string value of the entry widget.
"""
initialvalue = initialvalue or ""
dialog = QueryDialog(
prompt, title, initialvalue, parent=parent, **kwargs
)
dialog.show()
return dialog._result
@staticmethod
def get_integer(
prompt="",
title=" ",
initialvalue=None,
minvalue=None,
maxvalue=None,
parent=None,
**kwargs,
):
"""Request an integer type input from the user.
Parameters:
prompt (str):
A message to display in the message box above the entry
widget.
title (str):
The string displayed as the title of the message box. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
initialvalue (int):
The initial value in the entry widget.
minvalue (int):
The minimum allowed value.
maxvalue (int):
The maximum allowed value.
parent (Widget):
Makes the window the logical parent of the message box. The
messagebox is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
int:
The integer value of the entry widget.
"""
initialvalue = initialvalue or ""
dialog = QueryDialog(
prompt,
title,
initialvalue,
minvalue,
maxvalue,
datatype=int,
parent=parent,
**kwargs,
)
dialog.show()
return dialog._result
@staticmethod
def get_float(
prompt="",
title=" ",
initialvalue=None,
minvalue=None,
maxvalue=None,
parent=None,
**kwargs,
):
"""Request a float type input from the user.
Parameters:
prompt (str):
A message to display in the message box above the entry
widget.
title (str):
The string displayed as the title of the message box. This
option is ignored on Mac OS X, where platform guidelines
forbid the use of a title on this kind of dialog.
initialvalue (float):
The initial value in the entry widget.
minvalue (float):
The minimum allowed value.
maxvalue (float):
The maximum allowed value.
parent (Widget):
Makes the window the logical parent of the message box. The
messagebox is displayed on top of its parent window.
**kwargs (Dict):
Other optional keyword arguments.
Returns:
float:
The float value of the entry widget.
"""
initialvalue = initialvalue or ""
dialog = QueryDialog(
prompt,
title,
initialvalue,
minvalue,
maxvalue,
datatype=float,
parent=parent,
**kwargs,
)
dialog.show()
return dialog._result
@staticmethod
def get_font(parent=None, **kwargs):
"""Request a customized font
Parameters:
parent (Widget):
Makes the window the logical parent of the dialog box. The
dialog is displayed on top of its parent window.
**kwargs (Dict):
Other keyword arguments.
Returns:
Font:
A font object.
"""
dialog = FontDialog(parent=parent, **kwargs)
dialog.show()
return dialog.result
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venky4121994/ga-learner-dsmp-repo | Google-Play-Store-App-Rating/code.py | 1bef03489931eece0d5ecb9ce0501dfeb558dc59 | # --------------
#Importing header files
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#Code starts here
data = pd.read_csv(path)
data.hist(['Rating'])
data = data[data['Rating']<=5]
data.hist(['Rating'])
#Code ends here
# --------------
# code starts here
total_null = data.isnull().sum()
percent_null = (total_null/data.isnull().count())
missing_data = pd.concat([total_null,percent_null],keys=['Total','Percent'],axis=1)
print(missing_data)
data.dropna(inplace=True)
total_null_1 = data.isnull().sum()
percent_null_1 = (total_null_1/data.isnull().count())
missing_data_1 = pd.concat([total_null_1,percent_null_1],keys=['Total','Percent'],axis=1)
print(missing_data_1)
# code ends here
# --------------
#Code starts here
plt.figure(figsize=(10,20))
catplot = sns.catplot(x = "Category", y = "Rating", data=data, kind="box",height=10)
catplot.set_xticklabels(rotation=90)
plt.title('Rating vs Category [BoxPlot]',size = 20)
#Code ends here
# --------------
#Importing header files
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
#Code starts here
print(data['Installs'])
data['Installs'] = data['Installs'].str.replace('+','')
data['Installs'] = data['Installs'].str.replace(',','')
data['Installs'] = data['Installs'].astype('int32')
le = LabelEncoder()
data['Installs'] = le.fit_transform(data['Installs'])
graph = sns.regplot(data['Installs'],data['Rating'],data=data)
graph.set_title('Rating vs Installs [Boxplot]')
plt.show()
#Code ends here
# --------------
#Code starts here
print(data['Price'].value_counts())
data['Price'] = data['Price'].str.replace('$','')
data['Price'] = data['Price'].astype('float32')
graph2 = sns.regplot(data['Price'],data['Rating'],data=data)
graph2.set_title('Rating vs Price [RegPlot]')
#Code ends here
# --------------
#Code starts here
print(len(data['Genres'].unique()), "genres")
data['Genres'] = data['Genres'].str.split(';').str[0]
gr_mean = data[['Genres','Rating']].groupby(['Genres'],as_index=False).mean()
print(gr_mean.describe())
gr_mean=gr_mean.sort_values('Rating')
print(gr_mean.head(1))
print(gr_mean.head(1))
#Code ends here
# --------------
#Code starts here
data['Last Updated'] = pd.to_datetime(data['Last Updated'])
data['Last Updated Days'] = (data['Last Updated'].max()-data['Last Updated']).dt.days
plt.figure(figsize = (10,10))
sns.regplot(x="Last Updated Days", y="Rating",color='lightpink',data=data)
plt.title('Rating vs Last Updated [Regplot]',size =20)
#Code ends here
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Banguiskode/nerds | converters/brat2iob.py | 366420b2ec57bf790562de62a79f4973cbd6b3ed | import argparse
import operator
import os
import re
import shutil
import spacy
import tempfile
from nerds.utils import spans_to_tokens, get_logger
def segment_text_to_sentences(text_file, sentence_splitter):
""" Segment text into sentences. Text is provided by BRAT in .txt
file.
Args:
text_file (str): the full path to the BRAT .txt file.
sentence_splitter (spacy LM): SpaCy EN language model.
Returns:
sentences (list((int, int, str))): list of sentence spans.
Spans are triples of (start_offset, end_offset, text),
where offset is relative to the text.
"""
sentences = []
ftext = open(text_file, "r")
for line in ftext:
splits = sentence_splitter(line.strip())
for sent in splits.sents:
sentences.append((sent.start_char, sent.end_char, sent.text))
ftext.close()
return sentences
def parse_text_annotations(ann_file):
""" Parses BRAT annotations provided in the .ann file and converts them
to annotation spans of (start_position, end_position, entity_class).
Args:
ann_file (str): full path to the BRAT .ann file.
Returns:
annotations (list((int, int, str))): list of annotation spans.
Spans are triples of (start_offset, end_offset, entity_class)
where offset is relative to the text.
"""
annots = []
fann = open(ann_file, "r")
for line in fann:
cols = re.split(r"\s+", line.strip())
if not cols[0].startswith("T"):
continue
annots.append((int(cols[2]), int(cols[3]), cols[1]))
fann.close()
return annots
def apply_annotations(sentences, annotations, tokenizer):
""" Apply annotation spans to the sentence spans to create a list of tokens
and tags.
Args:
sentences (list((int, int, str))): list of sentence spans.
annotations (list((int, int, str))): list of annotation spans.
tokenizer (spacy LM): SpaCy EN language model.
Returns:
tokens_tags_list (list((list(str), list(str)))): list of list of token
tag pairs. Each list of token-tag pairs corresponds to a single
sentence.
"""
tokens_tags_list = []
for sent_start, sent_end, sent_text in sentences:
sent_annots = [a for a in annotations if a[0] >= sent_start and a[1] <= sent_end]
# convert document offsets to sentence offsets
sent_annots = [(s[0] - sent_start, s[1] - sent_start, s[2]) for s in sent_annots]
tokens, tags = spans_to_tokens(sent_text, sent_annots, tokenizer)
tokens_tags_list.append(zip(tokens, tags))
return tokens_tags_list
def convert_brat_to_iob(input_dir, output_file, nlp):
""" Convenience Convertor function.
Args:
input_dir (str): the directory where the BRAT .txt and .ann files
are located.
output_file (str): the full path name of file to write output in
IOB format to.
nlp (SpaCy LM): reference to the SpaCy EN model.
Returns:
None.
"""
fout = open(output_file, "w")
for text_file in os.listdir(input_dir):
# only process .txt and .ann pairs in specified directory
if not text_file.endswith(".txt"):
continue
annot_file = text_file[:-4] + ".ann"
if not os.path.exists(os.path.join(input_dir, annot_file)):
# do not process file if no corresponding .ann file
continue
# process file pair
logger.info("Processing file: {:s}".format(text_file))
sentences = segment_text_to_sentences(os.path.join(input_dir, text_file), nlp)
annotations = parse_text_annotations(os.path.join(input_dir, annot_file))
tokens_tags_list = apply_annotations(sentences, annotations, nlp)
for tokens_tags in tokens_tags_list:
for token, tag in tokens_tags:
fout.write("{:s}\t{:s}\n".format(token, tag))
fout.write("\n")
fout.close()
def do_self_test(nlp):
""" Simple self-test with small dataset to prove that this works okay. """
text = "Pierre Vinken, 61 years old, will join the board as a nonexecutive director, Nov. 29. Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group."
annotations = [
"T1 PER 0 13 Pierre Vinken",
"T2 PER 86 96 Mr. Vinken",
"T3 DATE 15 27 61 years old",
"T4 DATE 77 84 Nov. 29",
"T5 ORG 112 125 Elsevier N.V.",
"T6 NORP 131 136 Dutch"
]
input_dir = tempfile.mkdtemp(dir="/tmp")
ftext = open(os.path.join(input_dir, "test.txt"), "w")
ftext.write(text)
ftext.close()
fann = open(os.path.join(input_dir, "test.ann"), "w")
for line in annotations:
fann.write(line + "\n")
fann.close()
output_file = os.path.join(input_dir, "test.iob")
convert_brat_to_iob(input_dir, output_file, nlp)
fout = open(output_file, "r")
for line in fout:
logger.warn(line.strip())
shutil.rmtree(input_dir)
################################ main ################################
#
# usage: brat2iob.py [-h] [-i INPUT_DIR] [-o OUTPUT_FILE] [-t]
# Script to convert BRAT annotations to IOB (NERDS) format.
# optional arguments:
# -h, --help show this help message and exit
# -i INPUT_DIR, --input_dir INPUT_DIR
# Directory to store BRAT .txt and .ann files.
# -o OUTPUT_FILE, --output_file OUTPUT_FILE
# Output file to write IOB output to.
# -t, --test Runs self test.
######################################################################
parser = argparse.ArgumentParser(
description="Script to convert BRAT annotations to IOB (NERDS) format.")
parser.add_argument("-i", "--input_dir", help="Directory to store BRAT .txt and .ann files.")
parser.add_argument("-o", "--output_file", help="Output file to write IOB output to.")
parser.add_argument("-t", "--test", help="Runs self test.", action="store_true")
args = parser.parse_args()
logger = get_logger()
input_dir = args.input_dir
output_file = args.output_file
self_test = args.test
nlp = spacy.load("en")
if self_test:
logger.info("Executing self test...")
do_self_test(nlp)
else:
logger.info("Reading BRAT .txt and .ann files from: {:s}".format(input_dir))
logger.info("Writing IOB tokens/tags to file: {:s}".format(output_file))
convert_brat_to_iob(input_dir, output_file, nlp)
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zjsteyn/kraken | kraken/lib/util.py | eaa9f4290db5425ddf80d0aebfa3944713558ab5 | """
Ocropus's magic PIL-numpy array conversion routines. They express slightly
different behavior from PIL.Image.toarray().
"""
import unicodedata
import numpy as np
from PIL import Image
__all__ = ['pil2array', 'array2pil']
def pil2array(im: Image.Image, alpha: int = 0) -> np.array:
if im.mode == '1':
return np.array(im.convert('L'))
return np.array(im)
def array2pil(a: np.array) -> Image:
if a.dtype == np.dtype("B"):
if a.ndim == 2:
return Image.frombytes("L", (a.shape[1], a.shape[0]),
a.tostring())
elif a.ndim == 3:
return Image.frombytes("RGB", (a.shape[1], a.shape[0]),
a.tostring())
else:
raise Exception("bad image rank")
elif a.dtype == np.dtype('float32'):
return Image.frombytes("F", (a.shape[1], a.shape[0]), a.tostring())
else:
raise Exception("unknown image type")
def is_bitonal(im: Image.Image) -> bool:
"""
Tests a PIL.Image for bitonality.
Args:
im (PIL.Image.Image): Image to test
Returns:
True if the image contains only two different color values. False
otherwise.
"""
return im.getcolors(2) is not None and len(im.getcolors(2)) == 2
def get_im_str(im: Image.Image) -> str:
return im.filename if hasattr(im, 'filename') else str(im)
def is_printable(char: str) -> bool:
"""
Determines if a chode point is printable/visible when printed.
Args:
char (str): Input code point.
Returns:
True if printable, False otherwise.
"""
letters = ('LC', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu')
numbers = ('Nd', 'Nl', 'No')
punctuation = ('Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps')
symbol = ('Sc', 'Sk', 'Sm', 'So')
printable = letters + numbers + punctuation + symbol
return unicodedata.category(char) in printable
def make_printable(char: str) -> str:
"""
Takes a Unicode code point and return a printable representation of it.
Args:
char (str): Input code point
Returns:
Either the original code point, the name of the code point if it is a
combining mark, whitespace etc., or the hex code if it is a control
symbol.
"""
if not char or is_printable(char):
return char
elif unicodedata.category(char) in ('Cc', 'Cs', 'Co'):
return '0x{:x}'.format(ord(char))
else:
return unicodedata.name(char)
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hao44le/ico_top_holder_analysis | analysis/calculate_holding_amount.py | aeeab01c90e4446b424c52c33a68ccb814123121 | import sys
sys.path.insert(0,'..')
from data.whale_data import exchnage_accounts
from data.html_helper import check_if_address_name_exists
from data.whale_eth_tx_data import *
from data.whale_token_tx_data import identify_investor_type_token
holding_account = "holding_account"
deposit_account = 'deposit_account'
withdraw_account = "withdraw_account"
in_type = "IN"
out_type = "OUT"
all_acc_types = dict()
for acc in exchnage_accounts:
all_acc_types[acc] = exchange_type
def update_y_array(X,y,timestamp,amount):
target_index = 0
for i in range(len(X)):
x_time = X[i]
if timestamp < x_time:
target_index = i
break
for i in range(target_index,len(y)):
y[i] += amount
return y
def perform_bfs_on_accounts(out_txs,top_holder_type,acc,m_type='OUT'):
print("\t"+m_type)
unique_out = set()
for out in out_txs:
unique_out.add(out[3])
unique_out = list(unique_out)[:5]
for out in unique_out:
print("\t"+out)
if out not in all_acc_types:
investor_type = identify_investor_type(out)
if investor_type == affliate_type:
investor_type = identify_investor_type_token(out)
print("\t\t{}".format(investor_type))
else:
investor_type = all_acc_types[out]
if investor_type == exchange_type:
top_holder_type[acc] = deposit_account if m_type == "OUT" else withdraw_account
all_acc_types[out] = investor_type
if acc not in top_holder_type:
top_holder_type[acc] = holding_account
return top_holder_type
def calculate_holding_amount(X,escape_accounts,txs):
top_holder_type = dict()
for acc in txs:
tx = txs[acc]
if acc in escape_accounts:
continue
#如果当前账户从来没有向外打过token,ignore
out_txs = [item for item in tx if item[2] == 'OUT']
if len(out_txs) == 0:
print("\tholding account")
top_holder_type[acc] = holding_account
continue
# build all traxe Y: holding_amount, deposit_amount, withdraw_amount
amount_trace_y = [0] * len(X)
for holder in txs:
if holder in escape_accounts:
continue
if holder not in top_holder_type:
print("{} not identified! ".format(holder))
continue
holder_type = top_holder_type[holder]
holder_txs = txs[holder]
print("{} {}".format(holder,holder_type))
for tx in holder_txs:
[timestamp,from_a,tx_type,to_a,amount] = tx
if holder_type == holding_account:
if tx_type == in_type:
amount_trace_y = update_y_array(X,amount_trace_y,timestamp,amount)
else:
amount_trace_y = update_y_array(X,amount_trace_y,timestamp,-amount)
return amount_trace_y
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JBoRu/TextBox-1 | textbox/trainer/trainer.py | 0dcbaa153acc507e3d55075312d7ca5d23146e03 | # @Time : 2020/11/14
# @Author : Junyi Li, Gaole He
# @Email : [email protected]
# UPDATE:
# @Time : 2020/12/2, 2020/11/27, 2020/12/3, 2020/12/26
# @Author : Jinhao Jiang, Xiaoxuan Hu, Tianyi Tang, Jinhao Jiang
# @Email : [email protected], [email protected], [email protected], [email protected]
r"""
textbox.trainer.trainer
################################
"""
import os
import torch
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import copy
import math
from torch.utils.data import DataLoader
from time import time
from logging import getLogger
from textbox.module.Optimizer.optim import ScheduledOptim
from textbox.evaluator import NgramEvaluator, TranslationEvaluator, SummarizationEvaluator
from textbox.utils import ensure_dir, early_stopping
class AbstractTrainer(object):
r"""Trainer Class is used to manage the training and evaluation processes of text generation system models.
AbstractTrainer is an abstract class in which the fit() and evaluate() method should be implemented according
to different training and evaluation strategies.
"""
def __init__(self, config, model):
self.config = config
self.model = model
def fit(self, train_data):
r"""Train the model based on the train data.
"""
raise NotImplementedError('Method [next] should be implemented.')
def evaluate(self, eval_data):
r"""Evaluate the model based on the eval data.
"""
raise NotImplementedError('Method [next] should be implemented.')
class Trainer(AbstractTrainer):
r"""The basic Trainer for basic training and evaluation strategies in text generation systems.
This class defines common functions for training and evaluation processes of most text generation system models,
including fit(), evalute(), resume_checkpoint() and some other features helpful for model training and evaluation.
Generally speaking, this class can serve most text generation system models, If the training process of the model
is to simply optimize a single loss without involving any complex training strategies, such as adversarial learning,
pre-training and so on.
Initializing the Trainer needs two parameters: `config` and `model`. `config` records the parameters information
for controlling training and evaluation, such as `learning_rate`, `epochs`, `eval_step` and so on.
More information can be found in [placeholder]. `model` is the instantiated object of a Model Class.
"""
def __init__(self, config, model):
super(Trainer, self).__init__(config, model)
self.logger = getLogger()
self.learner = config['learner']
self.learning_rate = config['learning_rate']
self.epochs = config['epochs']
self.eval_step = min(config['eval_step'], self.epochs)
self.stopping_step = config['stopping_step']
self.test_batch_size = config['eval_batch_size']
self.device = config['device']
self.embedding_size = config['embedding_size']
self.warmup_steps = config['warmup_steps']
self.checkpoint_dir = config['checkpoint_dir']
ensure_dir(self.checkpoint_dir)
saved_model_file = self.config['filename'] + '.pth'
self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file)
self.generated_text_dir = config['generated_text_dir']
ensure_dir(self.generated_text_dir)
saved_text_file = self.config['filename'] + '.txt'
self.saved_text_file = os.path.join(self.generated_text_dir, saved_text_file)
self.start_epoch = 0
self.cur_step = 0
self.best_valid_score = 100000000
self.best_valid_result = None
self.train_loss_dict = dict()
self.optimizer = self._build_optimizer()
self.task_type = config['task_type'].lower()
if self.task_type == "translation":
self.evaluator = TranslationEvaluator(config)
elif self.task_type == "summarization":
self.evaluator = SummarizationEvaluator(config)
else:
self.evaluator = NgramEvaluator(config)
self.item_tensor = None
self.tot_item_num = None
self.iid_field = config['ITEM_ID_FIELD']
def _build_optimizer(self):
r"""Init the Optimizer
Returns:
torch.optim: the optimizer
"""
if self.learner.lower() == 'adam':
optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'sgd':
optimizer = optim.SGD(self.model.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'adagrad':
optimizer = optim.Adagrad(self.model.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'rmsprop':
optimizer = optim.RMSprop(self.model.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'schedule':
optimizer = ScheduledOptim(optim.Adam(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09),
self.learning_rate, self.embedding_size, self.warmup_steps)
else:
self.logger.warning('Received unrecognized optimizer, set default Adam optimizer')
optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
return optimizer
def _train_epoch(self, train_data, epoch_idx):
r"""Train the model in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.train()
total_loss = None
for batch_idx, data in enumerate(train_data):
self.optimizer.zero_grad()
losses = self.model.calculate_loss(data, epoch_idx=epoch_idx)
if isinstance(losses, tuple):
loss = sum(losses)
loss_tuple = tuple(per_loss.item() for per_loss in losses)
total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple)))
else:
loss = losses
total_loss = losses.item() if total_loss is None else total_loss + losses.item()
self._check_nan(loss)
loss.backward()
self.optimizer.step()
train_loss = total_loss / len(train_data)
return train_loss
def _valid_epoch(self, valid_data):
r"""Valid the model with valid data
Args:
valid_data (DataLoader): the valid data
Returns:
float: valid score
dict: valid result
"""
self.model.eval()
total_loss = None
for batch_idx, data in enumerate(valid_data):
losses = self.model.calculate_loss(data)
if isinstance(losses, tuple):
loss = sum(losses)
loss_tuple = tuple(per_loss.item() for per_loss in losses)
total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple)))
else:
loss = losses
total_loss = losses.item() if total_loss is None else total_loss + losses.item()
self._check_nan(loss)
valid_loss = total_loss / len(valid_data)
ppl = np.exp(valid_loss)
return valid_loss, ppl
def _save_checkpoint(self, epoch):
r"""Store the model parameters information and training information.
Args:
epoch (int): the current epoch id
"""
state = {
'config': self.config,
'epoch': epoch,
'cur_step': self.cur_step,
'best_valid_score': self.best_valid_score,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
}
torch.save(state, self.saved_model_file)
def _save_generated_text(self, generated_corpus):
r"""Store the generated text by our model.
Args:
corpus (list of string list):
"""
with open(self.saved_text_file, 'w') as fin:
for tokens in generated_corpus:
fin.write(' '.join(tokens) + '\n')
def resume_checkpoint(self, resume_file):
r"""Load the model parameters information and training information.
Args:
resume_file (file): the checkpoint file
"""
resume_file = str(resume_file)
checkpoint = torch.load(resume_file)
self.start_epoch = checkpoint['epoch'] + 1
self.cur_step = checkpoint['cur_step']
self.best_valid_score = checkpoint['best_valid_score']
# load architecture params from checkpoint
if checkpoint['config']['model'].lower() != self.config['model'].lower():
self.logger.warning('Architecture configuration given in config file is different from that of checkpoint. '
'This may yield an exception while state_dict is being loaded.')
self.model.load_state_dict(checkpoint['state_dict'])
# load optimizer state from checkpoint only when optimizer type is not changed
self.optimizer.load_state_dict(checkpoint['optimizer'])
message_output = 'Checkpoint loaded. Resume training from epoch {}'.format(self.start_epoch)
self.logger.info(message_output)
def _check_nan(self, loss):
if torch.isnan(loss):
raise ValueError('Training loss is nan')
def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""):
train_loss_output = "epoch %d %straining [time: %.2fs, " % (epoch_idx, train_info, e_time - s_time)
if isinstance(losses, tuple):
for idx, loss in enumerate(losses):
train_loss_output += 'train_loss%d: %.4f, ' % (idx + 1, loss)
train_loss_output = train_loss_output[:-2]
else:
train_loss_output += "train loss: %.4f" % losses
return train_loss_output + ']'
def fit(self, train_data, valid_data=None, verbose=True, saved=True):
r"""Train the model based on the train data and the valid data.
Args:
train_data (DataLoader): the train data
valid_data (DataLoader, optional): the valid data, default: None.
If it's None, the early_stopping is invalid.
verbose (bool, optional): whether to write training and evaluation information to logger, default: True
saved (bool, optional): whether to save the model parameters, default: True
Returns:
(float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None)
"""
for epoch_idx in range(self.start_epoch, self.epochs):
# train
training_start_time = time()
train_loss = self._train_epoch(train_data, epoch_idx)
self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
self._save_checkpoint(epoch_idx)
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss)
if verbose:
self.logger.info(train_loss_output)
# eval
if self.eval_step <= 0 or not valid_data:
if saved:
self._save_checkpoint(epoch_idx)
update_output = 'Saving current: %s' % self.saved_model_file
if verbose:
self.logger.info(update_output)
continue
if (epoch_idx + 1) % self.eval_step == 0:
valid_start_time = time()
with torch.no_grad():
valid_score, valid_result = self._valid_epoch(valid_data)
# valid_loss, ppl
self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping(
valid_score, self.best_valid_score, self.cur_step,
max_step=self.stopping_step, bigger=False)
# better model are supposed to provide smaller perplexity and loss
valid_end_time = time()
valid_score_output = "epoch %d evaluating [time: %.2fs, valid_loss: %f]" % \
(epoch_idx, valid_end_time - valid_start_time, valid_score)
valid_result_output = 'valid ppl: {}'.format(valid_result)
if verbose:
self.logger.info(valid_score_output)
self.logger.info(valid_result_output)
if update_flag:
if saved:
self._save_checkpoint(epoch_idx)
update_output = 'Saving current best: %s' % self.saved_model_file
if verbose:
self.logger.info(update_output)
self.best_valid_result = valid_result
if stop_flag:
stop_output = 'Finished training, best eval result in epoch %d' % \
(epoch_idx - self.cur_step * self.eval_step)
if verbose:
self.logger.info(stop_output)
break
return self.best_valid_score, self.best_valid_result
def _evaluate_nll_test(self, eval_data):
r"""Calculate the negative log-likelihood of the eval_data.
Args:
eval_data (DataLoader): the eval data.
Returns:
Float: NLL_test of the eval data.
"""
total_loss = 0
for epoch_idx, eval_batch in enumerate(eval_data):
nll_test = self.model.calculate_nll_test(eval_batch, epoch_idx)
total_loss += float(nll_test)
return total_loss / len(eval_data)
@torch.no_grad()
def evaluate(self, eval_data, load_best_model=True, model_file=None):
r"""Evaluate the model based on the eval data.
Args:
eval_data (DataLoader): the eval data
load_best_model (bool, optional): whether load the best model in the training process, default: True.
It should be set True, if users want to test the model after training.
model_file (str, optional): the saved model file, default: None. If users want to test the previously
trained model file, they can set this parameter.
Returns:
dict: eval result, key is the eval metric and value in the corresponding metric value
"""
if load_best_model:
if model_file:
checkpoint_file = model_file
else:
checkpoint_file = self.saved_model_file
checkpoint = torch.load(checkpoint_file)
self.model.load_state_dict(checkpoint['state_dict'])
message_output = 'Loading model structure and parameters from {}'.format(checkpoint_file)
self.logger.info(message_output)
self.model.eval()
with torch.no_grad():
generate_corpus = self.model.generate(eval_data)
self._save_generated_text(generate_corpus)
reference_corpus = eval_data.get_reference()
result = self.evaluator.evaluate(generate_corpus, reference_corpus)
result['nll_test'] = self._evaluate_nll_test(eval_data)
return result
def plot_train_loss(self, show=True, save_path=None):
r"""Plot the train loss in each epoch
Args:
show (bool, optional): whether to show this figure, default: True
save_path (str, optional): the data path to save the figure, default: None.
If it's None, it will not be saved.
"""
epochs = list(self.train_loss_dict.keys())
epochs.sort()
values = [float(self.train_loss_dict[epoch]) for epoch in epochs]
plt.plot(epochs, values)
plt.xticks(epochs)
plt.xlabel('Epoch')
plt.ylabel('Loss')
if show:
plt.show()
if save_path:
plt.savefig(save_path)
class UnconditionalTrainer(Trainer):
r"""UnconditionalTrainer is designed for RNN, which is a typical unconditional generator.
"""
def __init__(self, config, model):
super(UnconditionalTrainer, self).__init__(config, model)
class GANTrainer(Trainer):
r"""GANTrainer is designed for GAN, which is a generative adversarial net method.
"""
def __init__(self, config, model):
super(GANTrainer, self).__init__(config, model)
self.optimizer = None
self.g_optimizer = self._build_module_optimizer(self.model.generator)
self.d_optimizer = self._build_module_optimizer(self.model.discriminator)
self.grad_clip = config['grad_clip']
self.g_pretraining_epochs = config['g_pretraining_epochs']
self.d_pretraining_epochs = config['d_pretraining_epochs']
self.d_sample_num = config['d_sample_num']
self.d_sample_training_epochs = config['d_sample_training_epochs']
self.adversarail_training_epochs = config['adversarail_training_epochs']
self.adversarail_d_epochs = config['adversarail_d_epochs']
self.g_pretraining_loss_dict = dict()
self.d_pretraining_loss_dict = dict()
self.max_length = config['max_seq_length'] + 2
self.pad_idx = model.pad_idx
def _build_module_optimizer(self, module):
r"""Init the Module Optimizer
Args:
module (torch.nn.Mudule): Mudule class of torch.nn needed optimizer
Returns:
torch.optim: the optimizer
"""
if self.learner.lower() == 'adam':
optimizer = optim.Adam(module.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'sgd':
optimizer = optim.SGD(module.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'adagrad':
optimizer = optim.Adagrad(module.parameters(), lr=self.learning_rate)
elif self.learner.lower() == 'rmsprop':
optimizer = optim.RMSprop(module.parameters(), lr=self.learning_rate)
else:
self.logger.warning('Received unrecognized optimizer, set default Adam optimizer')
optimizer = optim.Adam(module.parameters(), lr=self.learning_rate)
return optimizer
def _optimize_step(self, losses, total_loss, model, opt):
r"""The opt uses the cliped losses to conduct an optimize step to optimize model
and sum up losses to the total_loss.
Args:
losses (torch.Tensor or tuple): The loss to be backward.
total_loss (Float): Total loss in an epoch.
model (torch.nn.Mudule): The model to be optimized.
opt (torch.optim): The optimizer of the model.
Returns:
torch.Tensor or tuple: Total loss in an epoch, shape: [].
"""
if isinstance(losses, tuple):
loss = sum(losses)
loss_tuple = tuple(per_loss.item() for per_loss in losses)
total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple)))
else:
loss = losses
total_loss = losses.item() if total_loss is None else total_loss + losses.item()
self._check_nan(loss)
opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
opt.step()
return total_loss
def _save_checkpoint(self, epoch):
state = {
'config': self.config,
'epoch': epoch,
'cur_step': self.cur_step,
'best_valid_score': self.best_valid_score,
'state_dict': self.model.state_dict()
}
torch.save(state, self.saved_model_file)
def _add_pad(self, data):
r"""Pad the data to the max length of corpus.
Args:
data (torch.Tensor): The data to be padded, shape: [batch_size, max_batch_length].
Returns:
torch.Tensor: The padded data, shape: [batch_size, max_seq_length].
"""
batch_size = data.shape[0]
padded_data = torch.full((batch_size, self.max_length), self.pad_idx, dtype=torch.long, device=self.device)
padded_data[:, : data.shape[1]] = data
return padded_data
def _get_real_data(self, train_data):
r"""Get the target text index of the corpus train_datas.
Args:
train_data (DataLoader): the train data.
Returns:
torch.Tensor: The target text index, shape: [batch_size, max_batch_length].
"""
real_datas = []
for corpus in train_data:
real_data = corpus['target_idx']
real_data = self._add_pad(real_data)
real_datas.append(real_data)
real_datas = torch.cat(real_datas, dim=0)
return real_datas
def _g_train_epoch(self, train_data, epoch_idx):
r"""Train the generator module in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.generator.train()
total_loss = None
for batch_idx, data in enumerate(train_data):
losses = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer)
total_loss = [l / len(train_data) for l in total_loss] if isinstance(total_loss, tuple) else total_loss / len(
train_data)
total_loss = tuple(total_loss) if isinstance(total_loss, list) else total_loss
return total_loss
def _d_train_epoch(self, train_data, epoch_idx):
r"""Train the discriminator module in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.discriminator.train()
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
fake_data = self.model.sample(self.d_sample_num)
fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for _ in range(self.d_sample_training_epochs): # d_epoch
for real_data, fake_data in zip(real_dataloader, fake_dataloader):
losses = self.model.calculate_d_train_loss(real_data, fake_data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer)
return total_loss / min(len(real_dataloader), len(fake_dataloader)) / self.d_sample_training_epochs
def _adversarial_train_epoch(self, train_data, epoch_idx):
r"""Adversarial training in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.generator.train()
total_loss = None
losses = self.model.calculate_g_adversarial_loss(epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer)
for epoch_idx in range(self.adversarail_d_epochs):
self._d_train_epoch(train_data, epoch_idx=epoch_idx)
return total_loss
def fit(self, train_data, valid_data=None, verbose=True, saved=True):
# generator pretraining
if verbose:
self.logger.info("Start generator pretraining...")
for epoch_idx in range(self.g_pretraining_epochs):
training_start_time = time()
train_loss = self._g_train_epoch(train_data, epoch_idx)
self.g_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss,
"generator pre")
if verbose:
self.logger.info(train_loss_output)
if verbose:
self.logger.info("End generator pretraining...")
# discriminator pretraining
if verbose:
self.logger.info("Start discriminator pretraining...")
for epoch_idx in range(self.d_pretraining_epochs):
training_start_time = time()
train_loss = self._d_train_epoch(train_data, epoch_idx)
self.d_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss,
"discriminator pre")
if verbose:
self.logger.info(train_loss_output)
if verbose:
self.logger.info("End discriminator pretraining...")
# adversarial training
if verbose:
self.logger.info("Start adversarial training...")
for epoch_idx in range(self.adversarail_training_epochs):
training_start_time = time()
train_loss = self._adversarial_train_epoch(train_data, epoch_idx)
self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss)
if verbose:
self.logger.info(train_loss_output)
if verbose:
self.logger.info("End adversarial pretraining...")
self._save_checkpoint(self.adversarail_training_epochs)
return -1, None
class TextGANTrainer(GANTrainer):
r"""TextGANTrainer is designed for TextGAN.
"""
def __init__(self, config, model):
super(TextGANTrainer, self).__init__(config, model)
self.adversarail_g_epochs = config['adversarail_g_epochs']
def _d_train_epoch(self, train_data, epoch_idx):
self.model.discriminator.train()
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for _ in range(self.d_sample_training_epochs):
for idx, real_data in enumerate(real_dataloader):
fake_data, z = self.model.sample()
losses = self.model.calculate_d_train_loss(real_data, fake_data, z, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer)
if (idx * self.model.batch_size >= self.d_sample_num):
break
return total_loss / min(len(real_dataloader), self.d_sample_num // self.model.batch_size) / self.d_sample_training_epochs
def _adversarial_train_epoch(self, train_data, epoch_idx):
self.model.generator.train()
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for idx, real_data in enumerate(real_dataloader):
if (idx == self.adversarail_g_epochs):
break
losses = self.model.calculate_g_adversarial_loss(real_data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer)
for epoch_idx in range(self.adversarail_d_epochs):
self._d_train_epoch(train_data, epoch_idx=epoch_idx)
return total_loss / min(len(real_dataloader), self.adversarail_g_epochs)
class RankGANTrainer(GANTrainer):
r"""RankGANTrainer is designed for RankGAN.
"""
def __init__(self, config, model):
super(RankGANTrainer, self).__init__(config, model)
def _d_train_epoch(self, train_data, epoch_idx):
r"""Train the discriminator module in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.discriminator.train()
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
fake_data = self.model.sample(self.d_sample_num)
fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
ref_index = np.random.randint(0, real_data.shape[0], size=self.model.ref_size)
ref_data = real_data[ref_index] # ref_size * l
for _ in range(self.d_sample_training_epochs):
for real_data, fake_data in zip(real_dataloader, fake_dataloader):
losses = self.model.calculate_d_train_loss(real_data, fake_data, ref_data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer)
return total_loss / min(len(real_dataloader), len(fake_dataloader)) / self.d_sample_training_epochs
def _adversarial_train_epoch(self, train_data, epoch_idx):
r"""Adversarial training in an epoch
Args:
train_data (DataLoader): the train data
epoch_idx (int): the current epoch id
Returns:
float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains
multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a
tuple which includes the sum of loss in each part.
"""
self.model.generator.train()
total_loss = None
real_data = self._get_real_data(train_data)
ref_index = np.random.randint(0, real_data.shape[0], size=self.model.ref_size)
ref_data = real_data[ref_index] # ref_size * l
losses = self.model.calculate_g_adversarial_loss(ref_data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer)
d_loss = 0
for epoch_idx in range(self.adversarail_d_epochs):
d_loss += self._d_train_epoch(train_data, epoch_idx=epoch_idx)
d_loss = d_loss / self.adversarail_d_epochs
return total_loss
class ConditionalTrainer(Trainer):
r"""ConditionalTrainer is designed for seq2seq testing, which is a typically used setting.
"""
def __init__(self, config, model):
super(ConditionalTrainer, self).__init__(config, model)
@torch.no_grad()
def evaluate(self, eval_data, load_best_model=True, model_file=None):
r"""Evaluate the model based on the eval data.
Args:
eval_data (DataLoader): the eval data
load_best_model (bool, optional): whether load the best model in the training process, default: True.
It should be set True, if users want to test the model after training.
model_file (str, optional): the saved model file, default: None. If users want to test the previously
trained model file, they can set this parameter.
Returns:
dict: eval result, key is the eval metric and value in the corresponding metric value
"""
if load_best_model:
if model_file:
checkpoint_file = model_file
else:
checkpoint_file = self.saved_model_file
checkpoint = torch.load(checkpoint_file)
self.model.load_state_dict(checkpoint['state_dict'])
message_output = 'Loading model structure and parameters from {}'.format(checkpoint_file)
self.logger.info(message_output)
self.model.eval()
generate_corpus = self.model.generate(eval_data)
self._save_generated_text(generate_corpus)
reference_corpus = eval_data.get_reference()
result = self.evaluator.evaluate(generate_corpus, reference_corpus)
return result
class MaskGANTrainer(GANTrainer):
r""" Trainer specifically designed for MaskGAN training process.
"""
def __init__(self, config, model):
super(MaskGANTrainer, self).__init__(config, model)
self.max_length = config["max_seq_length"]
self.eos_token_idx = model.eos_idx
self.adversarail_c_epochs = config['adversarail_c_epochs']
self.g_mask_pretraining_epochs = config['g_mask_pretraining_epochs']
self.g_lr = config['gen_learning_rate']
self.d_lr = config['dis_learning_rate']
self.c_lr = config['critic_learning_rate']
self.g_optimizer = self._build_module_optimizer_(self.model.generator, self.g_lr)
self.d_optimizer = self._build_module_optimizer_(self.model.discriminator, self.d_lr)
self.c_optimizer = self._build_module_optimizer_(self.model.discriminator.critic_fc_linear, self.c_lr)
self.pre_lm_weight = config["pre_lm_weight"]
self.pretrain_lm_epochs = config["pretrain_lm_epochs"]
self.checkp = config['checkp']
def _build_module_optimizer_(self, module, lr):
r""" Init the Module Optimizer with specified learning rate
Returns:
torch.optim: the optimizer
"""
if self.learner.lower() == 'adam':
optimizer = optim.Adam(module.parameters(), lr)
elif self.learner.lower() == 'sgd':
optimizer = optim.SGD(module.parameters(), lr)
elif self.learner.lower() == 'adagrad':
optimizer = optim.Adagrad(module.parameters(), lr)
elif self.learner.lower() == 'rmsprop':
optimizer = optim.RMSprop(module.parameters(), lr)
else:
self.logger.warning('Received unrecognized optimizer, set default Adam optimizer')
optimizer = optim.Adam(module.parameters(), lr)
return optimizer
def _optimize_step(self, losses, total_loss, model, opt, retain_graph=False):
r""" Add retain_graph option
"""
if isinstance(losses, tuple):
loss = sum(losses)
loss_tuple = tuple(per_loss.item() for per_loss in losses)
total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple)))
else:
loss = losses
total_loss = losses.item() if total_loss is None else total_loss + losses.item()
self._check_nan(loss)
opt.zero_grad()
loss.backward(retain_graph=retain_graph)
torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
opt.step()
return total_loss
def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""):
r""" Specified for maskgan output
"""
train_loss_output = "%straining [time: %.2fs, " % (train_info, e_time - s_time)
if isinstance(losses, dict):
for key, loss in losses.items():
train_loss_output += '%s: %.4f, ' % (key, loss)
train_loss_output = train_loss_output[:-2]
else:
train_loss_output += "train loss: %.4f" % losses
return train_loss_output + ']'
def pretrain_lm(self, train_data, valid_data, verbose):
r""" Pretrain rnn-based Language Model with teacher forcing mechanism
"""
def lm_forward(data):
r""" One iteration of LM forward
"""
input = data[:, :-1] # bs * self.max_len - 1
target = data[:, 1:]
bs, seq_len = target.size()
lengths = torch.tensor([seq_len] * bs)
target_present = torch.ones_like(input).byte()
device = target.device
lengths = lengths.cuda(device)
# pretaining
encoder_outputs = pre_train_lm(input, lengths, target, target_present, pretrain=True)
logit = pre_train_lm.vocab_linear(encoder_outputs)
logit = logit.permute([0, 2, 1])
lossf = torch.nn.CrossEntropyLoss()
loss = lossf(logit, target)
return loss
pre_train_lm = self.model.generator
lm_opt = self._build_module_optimizer_(pre_train_lm, lr=0.001)
for epoch in range(self.pretrain_lm_epochs):
total_loss = None
real_data = self._get_real_data(train_data) # bs * self.max_len
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(real_dataloader):
loss = lm_forward(data)
total_loss = self._optimize_step(loss, total_loss, pre_train_lm, lm_opt)
total_loss = total_loss / len(real_dataloader)
if verbose:
self.logger.info("Epoch {}/{} of LM pretraining loss: {} ".format(epoch+1, self.pretrain_lm_epochs, total_loss))
ppl = 0.0
if (epoch+1) % 1 == 0:
pre_train_lm.eval()
validate_data = self._get_real_data(valid_data) # bs * self.max_len
validate_dataloader = DataLoader(validate_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
ppl = 0.0
for batch_idx, data in enumerate(validate_dataloader):
cross_entropy_loss = lm_forward(data)
ppl += math.exp(cross_entropy_loss.item())
ppl = ppl / len(validate_dataloader)
pre_train_lm.train()
if verbose:
self.logger.info("Epoch {}/{} of LM pretraining PPL: {}...".format(epoch + 1, self.pretrain_lm_epochs, ppl))
if ppl < 110:
state_dict = {
'embedder': pre_train_lm.embedder,
'encoder': pre_train_lm.encoder.encoder,
'vocab_linear': pre_train_lm.vocab_linear
}
self.pre_lm_weight = "saved/pretrain_lm_weight" + str(epoch+1) + ".pkl"
torch.save(state_dict, self.pre_lm_weight)
if verbose:
self.logger.info("End LM pretraining. PPL: {}".format(ppl))
self.logger.info("Weigth saved in {}".format(self.pre_lm_weight))
return pre_train_lm, ppl
def _g_train_epoch(self, train_data, epoch_idx):
self.model.generator.train()
total_loss = None
real_data = self._get_real_data(train_data) # bs * self.max_len
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(real_dataloader):
loss = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(loss, total_loss, self.model.generator, self.g_optimizer)
total_loss = total_loss / len(real_dataloader)
return total_loss
def _get_validate_ppl(self, validate_data, epoch_idx):
self.model.generator.eval()
ppl = 0.0
validate_data = self._get_real_data(validate_data) # bs * self.max_len
validate_dataloader = DataLoader(validate_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(validate_dataloader):
loss = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx, validate=True)
ppl += math.exp(loss.item())
ppl = ppl / len(validate_dataloader)
self.model.generator.train()
return ppl
def _d_train_epoch(self, train_data, epoch_idx):
self.model.discriminator.train()
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(real_dataloader):
losses = self.model.calculate_d_train_loss(data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer)
return total_loss / len(real_dataloader)
def _adversarial_train_epoch(self, train_data, epoch_idx):
r""" Specified for MaskGAN adversarial training
"""
dis_total_loss = None
gen_total_loss = None
critic_total_loss = None
g_num = 0.0
d_num = 0.0
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
dis_train_data = copy.deepcopy(real_dataloader)
gen_train_data = copy.deepcopy(real_dataloader)
c_train_data = copy.deepcopy(real_dataloader)
dis_train_data = iter(dis_train_data)
gen_train_data = iter(gen_train_data)
_ = next(dis_train_data) # have one offset
for g_x in gen_train_data:
g_num += 1
for _ in range(3):
d_num += 1
try:
d_x = next(dis_train_data)
except StopIteration:
del dis_train_data
dis_train_data = copy.deepcopy(real_dataloader)
dis_train_data = iter(dis_train_data)
d_x = next(dis_train_data)
losses = self.model.calculate_d_train_loss(d_x, epoch_idx=_)
dis_total_loss = self._optimize_step(losses, dis_total_loss, self.model.discriminator, self.d_optimizer)
gen_losses, critic_losses = self.model.calculate_g_adversarial_loss(g_x, epoch_idx=g_num)
gen_total_loss = self._optimize_step(gen_losses, gen_total_loss, self.model.generator, self.g_optimizer)
critic_total_loss = self._optimize_step(critic_losses, critic_total_loss, self.model.discriminator.critic_fc_linear, self.c_optimizer)
return {"dis_loss": dis_total_loss / d_num, "gen_loss": gen_total_loss / g_num, "critic_loss": critic_total_loss / g_num}
def _evaluate_nll_test(self, eval_data):
total_loss = 0
real_data = self._get_real_data(eval_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(real_dataloader):
nll_test = self.model.calculate_nll_test(data, batch_idx)
total_loss += float(nll_test)
return total_loss / len(eval_data)
def _add_eos(self, data, length):
batch_size, pad_seq_len = data.size()
padded_data = torch.full((batch_size, self.max_length), self.eos_token_idx, dtype=torch.long, device=self.device)
for i in range(batch_size):
l = int(length[i].cpu().data)
if l == self.max_length+2:
padded_data[i, :] = data[i, 1:l-1]
else:
padded_data[i, 0:l-1] = data[i, 1:l]
return padded_data
def _get_real_data(self, train_data):
real_datas = []
for corpus in train_data:
real_data = corpus['target_idx'] # bs*batch_max_seq_len
length = corpus['target_length']
real_data = self._add_eos(real_data, length)
real_datas.append(real_data)
real_datas = torch.cat(real_datas, dim=0)
return real_datas
def _save_checkpoint(self, epoch, postfix=None):
state = {
'config': self.config,
'epoch': epoch,
'cur_step': self.cur_step,
'best_valid_score': self.best_valid_score,
'state_dict': self.model.state_dict(),
'g_opt': self.g_optimizer.state_dict(),
'd_opt': self.d_optimizer.state_dict(),
'c_opt':self.c_optimizer.state_dict()
}
if postfix is not None:
path = self.saved_model_file + "_" + str(epoch) + "_" + postfix
torch.save(state, path)
return path
else:
torch.save(state, self.saved_model_file)
def _load_generated_text(self):
r""" Load the generated text by our model to log.
"""
with open(self.saved_text_file, 'r') as fin:
samples = []
for i in range(5):
text = fin.readline()
samples.append(text)
return samples
def fit(self, train_data, valid_data=None, verbose=True, saved=True):
# generator pretraining
if self.checkp is not None:
checkpoint = torch.load(self.checkp)
self.model.load_state_dict(checkpoint['state_dict'])
self.d_optimizer.load_state_dict(checkpoint["d_opt"])
self.g_optimizer.load_state_dict(checkpoint["g_opt"])
epoch_check = checkpoint['epoch']
if verbose:
self.logger.info("Load checkpoint file from: {}".format(self.checkp))
else:
if self.pre_lm_weight is None:
if verbose:
self.logger.info("Start LM pretraining...")
pretrain_lm, ppl = self.pretrain_lm(train_data, valid_data, verbose)
pretrain_lm = torch.load(self.pre_lm_weight)
embedder = pretrain_lm['embedder'].state_dict()
lstm = pretrain_lm['encoder'].state_dict()
vocab_linear = pretrain_lm['vocab_linear'].state_dict()
self.model.generator.embedder.load_state_dict(embedder)
self.model.generator.encoder.encoder.load_state_dict(lstm)
self.model.generator.decoder.decoder.load_state_dict(lstm)
self.model.generator.vocab_linear.load_state_dict(vocab_linear)
self.model.discriminator.encoder.encoder.load_state_dict(lstm)
self.model.discriminator.decoder.decoder.load_state_dict(lstm)
if verbose:
self.logger.info("Load pretrained LM weight")
else:
pretrain_lm = torch.load(self.pre_lm_weight)
embedder = pretrain_lm['embedder'].state_dict()
lstm = pretrain_lm['encoder'].state_dict()
vocab_linear = pretrain_lm['vocab_linear'].state_dict()
self.model.generator.embedder.load_state_dict(embedder)
self.model.generator.encoder.encoder.load_state_dict(lstm)
self.model.generator.decoder.decoder.load_state_dict(lstm)
self.model.generator.vocab_linear.load_state_dict(vocab_linear)
self.model.discriminator.encoder.encoder.load_state_dict(lstm)
self.model.discriminator.decoder.decoder.load_state_dict(lstm)
if verbose:
self.logger.info("Load pretrained LM weight from: {}".format(self.pre_lm_weight))
if verbose:
self.logger.info("Start generator mask pretraining...")
for epoch_idx in range(self.g_mask_pretraining_epochs):
training_start_time = time()
train_loss = self._g_train_epoch(train_data, epoch_idx)
self.g_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss,
"generator pre")
if verbose:
self.logger.info(train_loss_output)
ppl = self._get_validate_ppl(valid_data, epoch_idx)
if verbose:
self.logger.info(
"Epoch {}/{} of mask pretraining PPL: {}...".format(epoch_idx + 1, self.g_mask_pretraining_epochs, ppl))
if ppl <= 90:
if verbose:
path = self._save_checkpoint(epoch_idx + 1, postfix="pretrain_gen")
self.logger.info(">>>> [Pretrain Gen] PPL: {} save weight in {}".format(ppl, path))
self.logger.info("End generator mask pretraining...")
break
if (epoch_idx) % 10 == 0:
self.logger.info(">>>> [Pretrain Gen] Save pretrain gen check in epoch %d ..." % (epoch_idx + 1))
path = self._save_checkpoint(epoch_idx + 1, postfix="pretrain_gen")
self.model.eval()
test_result = self.evaluate(valid_data, model_file=path)
self.model.train()
sample = self._load_generated_text()
tmp = "\n"
for i, s in enumerate(sample):
tmp += str(i)
tmp += ": "
tmp += s.strip()
tmp += "\n"
self.logger.info('>>>> [Pretrain Gen] test result: {}'.format(test_result))
self.logger.info('>>>> [Pretrain Gen] test result samples: {}'.format(tmp))
# discriminator pretraining
if verbose:
self.logger.info("Start discriminator pretraining...")
for epoch_idx in range(self.d_pretraining_epochs):
training_start_time = time()
train_loss = self._d_train_epoch(train_data, epoch_idx)
self.d_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss,
"discriminator pre")
if verbose:
self.logger.info(train_loss_output)
if verbose:
self.logger.info("End discriminator pretraining...")
# adversarial training
if verbose:
self.logger.info("Start adversarial training...")
for epoch_idx in range(self.adversarail_training_epochs):
training_start_time = time()
train_loss = self._adversarial_train_epoch(train_data, epoch_idx)
self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss)
if verbose:
self.logger.info(train_loss_output)
if (epoch_idx+1) % 10 == 0:
path = self._save_checkpoint((epoch_idx + 1), postfix="adv_train")
self.model.eval()
test_result = self.evaluate(valid_data, model_file=path)
self.model.train()
sample = self._load_generated_text()
tmp = "\n"
for i, s in enumerate(sample):
tmp += str(i)
tmp += ": "
tmp += s.strip()
tmp += "\n"
self.logger.info('>>>>>> [Adv] test result: {}'.format(test_result))
self.logger.info('>>>>>> [Adv] test result samples: {}'.format(tmp))
if verbose:
self.logger.info("End adversarial pretraining...")
self._save_checkpoint(self.adversarail_training_epochs)
return -1, None
class LeakGANTrainer(GANTrainer):
r"""Specified for leakgan trainer
"""
def __init__(self, config, model):
super(LeakGANTrainer, self).__init__(config, model)
self.interleaved_pretrain_epoch = config['interleaved_pretrain_epoch']
self.adversarail_g_epochs = config['adversarail_g_epochs']
gen_lr = config['generator_lr'] # 0.001
dis_lr = config['discriminator_lr'] # 0.00005
self.g_optimizer = self._build_module_optimizer_(self.model.generator, gen_lr) # (manager_opt, worker_opt)
self.d_optimizer = self._build_module_optimizer_(self.model.discriminator, dis_lr)
self.iters_num = config['iter_num']
self.end_idx = model.end_idx
def _build_module_optimizer_(self, module, learing_rate):
r"""Specified for leakgan
"""
multi_flag = False
if module._get_name() == 'LeakGANGenerator':
manager_params, worker_params = module.split_params()
multi_flag = True
if self.learner.lower() == 'adam':
if multi_flag:
manager_opt = optim.Adam(manager_params, lr=learing_rate)
worker_opt = optim.Adam(worker_params, lr=learing_rate)
else:
optimizer = optim.Adam(module.parameters(), lr=learing_rate)
elif self.learner.lower() == 'sgd':
if multi_flag:
manager_opt = optim.SGD(manager_params, lr=learing_rate)
worker_opt = optim.SGD(worker_params, lr=learing_rate)
else:
optimizer = optim.SGD(module.parameters(), lr=learing_rate)
elif self.learner.lower() == 'adagrad':
if multi_flag:
manager_opt = optim.Adagrad(manager_params, lr=learing_rate)
worker_opt = optim.Adagrad(worker_params, lr=learing_rate)
else:
optimizer = optim.Adagrad(module.parameters(), lr=learing_rate)
elif self.learner.lower() == 'rmsprop':
if multi_flag:
manager_opt = optim.RMSprop(manager_params, lr=learing_rate)
worker_opt = optim.RMSprop(worker_params, lr=learing_rate)
else:
optimizer = optim.RMSprop(module.parameters(), lr=learing_rate)
else:
self.logger.warning('Received unrecognized optimizer, set default Adam optimizer')
if multi_flag:
manager_opt = optim.Adam(manager_params, lr=learing_rate)
worker_opt = optim.Adam(worker_params, lr=learing_rate)
else:
optimizer = optim.Adam(module.parameters(), lr=learing_rate)
if multi_flag:
return (manager_opt, worker_opt)
else:
return optimizer
def _optimize_step(self, losses, total_loss, model, opt):
r"""Specified for leakgan optimize
"""
if isinstance(losses, tuple):
loss = sum(losses)
loss_tuple = tuple(per_loss.item() for per_loss in losses)
total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple)))
else:
loss = losses
total_loss = losses.item() if total_loss is None else total_loss + losses.item()
self._check_nan(loss)
if isinstance(losses, tuple):
for i, (o, loss) in enumerate(zip(opt, losses)):
o.zero_grad()
loss.backward(retain_graph=True if i < len(opt) - 1 else False)
torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
o.step()
else:
opt.zero_grad()
losses.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
opt.step()
return total_loss
def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""):
r"""Specified for leakgan output format
"""
train_loss_output = "%straining [time: %.2fs, " % (train_info, e_time - s_time)
if isinstance(losses, dict):
for key, loss in losses.items():
train_loss_output += '%s: %.4f, ' % (key, loss)
train_loss_output = train_loss_output[:-2]
else:
train_loss_output += "train loss: %.4f" % losses
return train_loss_output + ']'
def _add_eos(self, data, length):
batch_size = data.shape[0]
padded_data = torch.full((batch_size, self.max_length), self.end_idx, dtype=torch.long, device=self.device)
for i in range(batch_size):
len = length[i].cpu().data
padded_data[i, :len] = data[i, :len]
return padded_data
def _get_real_data(self, train_data):
r"""Specified for leakgan which use eos_idx pad not pad_idx
"""
real_datas = []
for corpus in train_data:
real_data = corpus['target_idx']
length = corpus['target_length']
real_data = self._add_eos(real_data, length)
real_datas.append(real_data)
real_datas = torch.cat(real_datas, dim=0)
return real_datas
def _adversarial_train_epoch(self, train_data, epoch_idx):
r"""Specified for leakgan adversarial training
"""
self.model.generator.train()
total_g_loss = None
total_d_loss = 0
total_d_acc = 0
adv_mana_loss = 0
adv_work_loss = 0
adv_d_loss = 0
for e in range(self.adversarail_g_epochs):
losses = self.model.calculate_g_adversarial_loss(epoch_idx=e)
total_g_loss = self._optimize_step(losses, total_g_loss, self.model.generator, self.g_optimizer)
adv_mana_loss, adv_work_loss = total_g_loss
adv_mana_loss = adv_mana_loss / self.adversarail_g_epochs
adv_work_loss = adv_work_loss / self.adversarail_g_epochs
for e in range(self.adversarail_d_epochs):
loss_dict = self._d_train_epoch(train_data, epoch_idx=epoch_idx)
total_d_loss = total_d_loss + loss_dict['total_loss']
total_d_acc = total_d_acc + loss_dict['train_acc']
adv_d_loss = total_d_loss / self.adversarail_d_epochs
adv_c_loss = total_d_acc / self.adversarail_d_epochs
return {"mana_loss": adv_mana_loss, "work_loss": adv_work_loss, "dis_loss": adv_d_loss, "train_acc": adv_c_loss}
def _g_train_epoch(self, train_data, epoch_idx):
total_loss = None
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
for batch_idx, data in enumerate(real_dataloader):
# interaction = interaction.to(self.device)
losses = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer)
total_loss = [l / len(real_dataloader) for l in total_loss] if isinstance(total_loss,
tuple) else total_loss / len(
train_data)
mana_loss, work_loss = total_loss
return {"mana_loss": mana_loss, "work_loss": work_loss}
def _d_train_epoch(self, train_data, epoch_idx):
total_loss = None
total_acc = 0
real_data = self._get_real_data(train_data)
real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
# not need sample self.d_sample_num numbers becauese only train discriminator 5 batch
d_sample_num = (self.d_sample_training_epochs + 1) * self.model.batch_size
fake_data = self.model.sample(d_sample_num)
fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True)
idx = 0
for real_data, fake_data in zip(real_dataloader, fake_dataloader):
# self.model.discriminator.eval() # pretraining not use dropout
if idx == self.d_sample_training_epochs:
break
losses, acc = self.model.calculate_d_train_loss(real_data, fake_data, epoch_idx=epoch_idx)
total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer)
total_acc = total_acc + acc
idx += 1
total_loss = total_loss / self.d_sample_training_epochs
total_acc = total_acc / self.d_sample_training_epochs
return {"total_loss": total_loss, "train_acc": total_acc}
def fit(self, train_data, valid_data=None, verbose=True, saved=True):
# pretraining
if verbose:
self.logger.info(">> Start pretraining")
# generator pretraining
for epoch_idx in range(self.g_pretraining_epochs): # 80
if verbose:
self.logger.info(">>>> [Pretrain Gen] Start %d / %d epochs generator pretraining" % (
epoch_idx + 1, self.g_pretraining_epochs))
training_start_time = time()
train_loss = self._g_train_epoch(train_data, epoch_idx)
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx + 1, training_start_time, training_end_time, train_loss,
"generator pre")
train_loss_output = ">>>> " + train_loss_output
if verbose:
self.logger.info(train_loss_output)
# discriminator pretraining
for epoch_idx in range(self.d_pretraining_epochs): # 5
if verbose:
self.logger.info(">>>> [Pretrain Dis]Start %d / %d epochs discriminator pretraining..." % (
epoch_idx + 1, self.d_pretraining_epochs))
training_start_time = time()
train_loss = self._d_train_epoch(train_data, epoch_idx)
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss,
"discriminator pre")
train_loss_output = ">>>> " + train_loss_output
if verbose:
self.logger.info(train_loss_output)
if verbose:
self.logger.info(">> End pretraining")
# adversarial training
if verbose:
self.logger.info(">> Start adversarial training")
for epoch in range(int(self.iters_num / self.adversarail_training_epochs)):
if verbose:
self.logger.info(">>>> [Adv] Start epoch %d / 10 interleaved adversarial training" % (epoch + 1))
for epoch_idx in range(self.adversarail_training_epochs):
if verbose:
self.logger.info(">>>>>> [Adv] Start epoch %d / %d adversarial training" % (
epoch_idx + 1, self.adversarail_training_epochs))
training_start_time = time()
train_loss = self._adversarial_train_epoch(train_data, epoch_idx)
# self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time,
train_loss,
train_info="adv ")
train_loss_output = ">>>>>> " + train_loss_output
if verbose:
self.logger.info(train_loss_output)
# gen pretrain
for epoch_idx in range(5):
if verbose:
self.logger.info(">>>>>> [Adv] Start epoch %d / 5 pretrain generator" % (epoch_idx + 1))
training_start_time = time()
train_loss = self._g_train_epoch(train_data, epoch_idx)
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time,
train_loss,
"adv generator pre")
train_loss_output = ">>>>>> " + train_loss_output
if verbose:
self.logger.info(train_loss_output)
# dis pretrain
for epoch_idx in range(5): # d_steps
if verbose:
self.logger.info(">>>>>> [Adv] Start epoch %d / 5 pretrain discriminator" % (epoch_idx + 1))
training_start_time = time()
train_loss = self._d_train_epoch(train_data, epoch_idx)
training_end_time = time()
train_loss_output = \
self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time,
train_loss,
"adv discriminator pre")
train_loss_output = ">>>>>> " + train_loss_output
if verbose:
self.logger.info(train_loss_output)
self._save_checkpoint(self.adversarail_training_epochs)
return -1, None
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EurusEurus/RSSerpent | rsserpent/plugins/builtin/__init__.py | fd7aaf67b80b2b48c14b1a3efe733374b0012338 | from ...models import Persona, Plugin
from . import example, example_cache, example_ratelimit, example_with_args
plugin = Plugin(
name="rsserpent-plugin-builtin",
author=Persona(
name="queensferryme",
link="https://github.com/queensferryme",
email="[email protected]",
),
repository="https://github.com/RSSerpent/RSSerpent",
prefix="/_",
routers={
example.path: example.provider,
example_cache.path: example_cache.provider,
example_ratelimit.path: example_ratelimit.provider,
example_with_args.path: example_with_args.provider,
},
)
__all__ = ("plugin",)
| [] |
luisroel91/libdib_assesment | data_processing/process_xls.py | c969cfecbce1243b457961ffafe5caaea7bb5149 | import pandas as pd
# Define our header
col_names = [
"year",
"num_males_with_income",
"male_median_income_curr_dollars",
"male_median_income_2019_dollars",
"num_females_with_income",
"female_median_income_curr_dollars",
"female_median_income_2019_dollars",
]
# Load Asian census data XLS, skipping all headers
dfa = pd.read_excel(
r'p08a.xlsx',
skiprows=8,
# Make sure PD doesn't use header row for our DF
header=None,
# Define col names
names=col_names,
)
# Load White census data XLS, skipping all headers
dfw = pd.read_excel(
r'p08w.xlsx',
skiprows=8,
# Make sure PD doesn't use header row for our DF
header=None,
# Define cold names
names=col_names
)
# Splinter off rows into age group DFs for both sets of data
dfa1524 = dfa.iloc[:20]
dfa2534 = dfa.iloc[25:45]
dfa3544 = dfa.iloc[50:70]
dfa4554 = dfa.iloc[75:95]
dfa5564 = dfa.iloc[100:120]
dfa6574 = dfa.iloc[125:145]
dfa75 = dfa.iloc[150:170]
dfw1524 = dfw.iloc[:20]
dfw2534 = dfw.iloc[25:45]
dfw3544 = dfw.iloc[50:70]
dfw4554 = dfw.iloc[75:95]
dfw5564 = dfw.iloc[100:120]
dfw6574 = dfw.iloc[125:145]
dfw75 = dfw.iloc[150:170]
# Add Age Range col to each DF
dfa1524.insert(0, 'age_range', '15-24')
dfa2534.insert(0, 'age_range', '25-34')
dfa3544.insert(0, 'age_range', '35-44')
dfa4554.insert(0, 'age_range', '45-54')
dfa5564.insert(0, 'age_range', '55-64')
dfa6574.insert(0, 'age_range', '65-74')
dfa75.insert(0, 'age_range', 'Over 75')
dfw1524.insert(0, 'age_range', '15-24')
dfw2534.insert(0, 'age_range', '25-34')
dfw3544.insert(0, 'age_range', '35-44')
dfw4554.insert(0, 'age_range', '45-54')
dfw5564.insert(0, 'age_range', '55-64')
dfw6574.insert(0, 'age_range', '65-74')
dfw75.insert(0, 'age_range', 'Over 75')
# Stack cleaned DF's vertically
dfa = pd.concat([
dfa1524,
dfa2534,
dfa3544,
dfa4554,
dfa5564,
dfa6574,
dfa75
], axis=0)
dfw = pd.concat([
dfw1524,
dfw2534,
dfw3544,
dfw4554,
dfw5564,
dfw6574,
dfw75
], axis=0)
# Add Race col
dfa.insert(0, 'race', 'asian')
dfw.insert(0, 'race', 'white')
# Clean garbage chars in Year col using regex
dfa['year'] = dfa['year'].replace(to_replace=r'(\s\(\d+\))', value='', regex=True)
dfw['year'] = dfw['year'].replace(to_replace=r'(\s\(\d+\))', value='', regex=True)
# Stack our cleaned + normalized data into a single DF
df = pd.concat([
dfa,
dfw
], axis=0)
# Convert the DF col types to conform to our CensusRecord model
df = df.astype({
"race": str,
"age_range": str,
"year": int,
"num_males_with_income": int,
"male_median_income_curr_dollars": float,
"male_median_income_2019_dollars": float,
"num_females_with_income": int,
"female_median_income_curr_dollars": float,
"female_median_income_2019_dollars": float,
})
# Pickle the DF
df.to_pickle("./res.pkl")
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Szymon-Budziak/WDI_exercises_solutions | Section_1/Exercise_16.py | 51ffc9ec8b3cd6809bd55e98ecb8aed759c2d460 | """
Dany jest ciąg określony wzorem: A[n+1] = (A[n] % 2) ∗ (3 ∗ A[n] + 1) + (1 − A[n] % 2) ∗ A[n] / 2.
Startując z dowolnej liczby naturalnej > 1 ciąg ten osiąga wartość 1. Napisać program, który
znajdzie wyraz początkowy z przedziału 2-10000 dla którego wartość 1 jest osiągalna po największej
liczbie kroków.
"""
a0 = 2
m = 1
for a0 in range(2, 10000):
n = 0
while a0 != 1:
a0 = (((a0 % 2) * (3 * a0 + 1)) + ((1 - (a0 % 2)) * (a0 / 2)))
n += 1
if n > m:
m = n
a0 += 1
print(m)
| [] |
evlog/SysPy | SysPy_ver/funcs/_var_declaration.py | d1ee6e2ca60492d20339c0016a9c24d027170553 | """
*****************************************************************************
*
H E A D E R I N F O R M A T I O N *
*
*****************************************************************************
Project Name: SysPy (System Python)
http://cgi.di.uoa.gr/~evlog/syspy.html
File Name: _var_declaration.py
Created by: Evangelos Logaras
*****************************************************************************
*
C O P Y R I G H T N O T I C E *
*
*****************************************************************************
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation;
version 2.1 of the License, a copy of which is available from
http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301
USA
*****************************************************************************
*
D E S C R I P T I O N *
*
*****************************************************************************
Variable declaration when a variable assignment is tracked.
"""
from pdb import *
def var_declaration(assign_lines_count, token_struct, assign_lines, signals, process_vars):
"""
FUNCTION: var_declaration(a int, b(), c[], d[], e[])
a: assign lines counter integer
b: token's tupple
c: list containing the VHDL code
d: list containing the signal statements
e: list containing
Variable declaration when a variable assignment is tracked.
"""
# Python's variable declerations
#----------------------------------------------------------------------------------------------------------------------------------
count0 = 0
count1 = 0
process_vars_d = []
vars0 = []
var0 = ''
var1 = ''
#----------------------------------------------------------------------------------------------------------------------------------
print("process_vars:", process_vars)
# Erasing duplicated registrations in "process_vars[]"
#----------------------------------------------------------------------------------------------------------------------------------
for i in range(len(process_vars)):
vars0 = []
#flag_process_vars = 0
if ((process_vars[i][0] == "name_left") or (process_vars[i][0] == "name_right")):
var0 = process_vars[i][1].replace('=', '')
var0 = var0.replace('! ', '')
var0 = var0.replace('>', '')
var0 = var0.replace('<', '')
var0 = var0.replace(' ', '')
vars0.append(var0)
elif (process_vars[i][0] == "name_right_binary_slice"):
var0 = process_vars[i][1][0]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_binary_slice_var0"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_binary_slice_var1"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_binary_slice_var01"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_item"):
var0 = process_vars[i][1][0]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_item_var"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_item"):
var0 = process_vars[i][1][0]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_item_var0"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_item_var1"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_item_var01"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice"):
var0 = process_vars[i][1][0]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var0"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var1"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var2"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][3]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var01"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var02"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][1]
vars0.append(var0)
var0 = process_vars[i][1][3]
vars0.append(var0)
elif (process_vars[i][0] == "name_right_array_binary_slice_var12"):
var0 = process_vars[i][1][0]
vars0.append(var0)
var0 = process_vars[i][1][2]
vars0.append(var0)
var0 = process_vars[i][1][3]
vars0.append(var0)
flag_process_vars = 0
for n in range(0, len(vars0)):
for j in range(len(process_vars_d)):
if ((process_vars_d[j][0] == "name_left") or (process_vars_d[j][0] == "name_right")):
var1 = process_vars_d[j][1].replace('=', '')
var1 = var1.replace('! ', '')
var1 = var1.replace('>', '')
var1 = var1.replace('<', '')
var1 = var1.replace(' ', '')
elif (process_vars_d[j][0] == "name_right_binary_slice"):
var1 = process_vars_d[j][1][0]
elif (process_vars_d[j][0] == "name_right_binary_slice_var0"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_binary_slice_var1"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_binary_slice_var01"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_item"):
var1 = process_vars_d[j][1][0]
elif (process_vars_d[j][0] == "name_right_item_var"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_item"):
var1 = process_vars_d[j][1][0]
elif (process_vars_d[j][0] == "name_right_array_binary_item_var0"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_item_var1"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_item_var01"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice"):
var1 = process_vars_d[j][1][0]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var0"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var1"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var2"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var01"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var02"):
var1 = process_vars_d[j][1]
elif (process_vars_d[j][0] == "name_right_array_binary_slice_var12"):
var1 = process_vars_d[j][1]
if (vars0[n] == var1):
if (n == 0):
flag_process_vars += 1
if (n == 1):
flag_process_vars += 2
if (n == 2):
flag_process_vars += 4
if ((process_vars[i][0] == "name_left") or (process_vars[i][0] == "name_right")):
if (flag_process_vars == 0):
process_vars_d.append(process_vars[i])
elif (process_vars[i][0] == "name_right_binary_slice"):
if (flag_process_vars == 0):
process_vars_d.append(process_vars[i])
elif (process_vars[i][0] == "name_right_binary_slice_var0"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_binary_slice_var0", process_vars[i][1][0]])
process_vars_d.append(["name_right_binary_slice_var0", process_vars[i][1][1]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_binary_slice_var0", process_vars[i][1][1]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_binary_slice_var0", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_binary_slice_var1"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_binary_slice_var1", process_vars[i][1][0]])
process_vars_d.append(["name_right_binary_slice_var1", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_binary_slice_var1", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_binary_slice_var1", process_vars[i][1][0]])
elif (flag_process_vars == 4):
pass
elif (process_vars[i][0] == "name_right_binary_slice_var01"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 3):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 4):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][1]])
elif (flag_process_vars == 5):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][1]])
elif (flag_process_vars == 6):
process_vars_d.append(["name_right_binary_slice_var01", process_vars[i][1][0]])
elif (flag_process_vars == 7):
pass
elif (process_vars[i][0] == "name_right_item"):
if (flag_process_vars == 0):
process_vars_d.append(process_vars[i])
elif (process_vars[i][0] == "name_right_item_var"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_item_var", process_vars[i][1][0]])
process_vars_d.append(["name_right_item_var", process_vars[i][1][1]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_item_var", process_vars[i][1][1]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_item_var", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_item"):
if (flag_process_vars == 0):
process_vars_d.append(process_vars[i])
elif (process_vars[i][0] == "name_right_array_binary_item_var0"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_item_var0", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_item_var0", process_vars[i][1][1]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_item_var0", process_vars[i][1][1]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_item_var0", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_item_var1"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_item_var1", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_item_var1", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_item_var1", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_item_var1", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_item_var01"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][2]])
elif (flag_process_vars == 3):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][2]])
elif (flag_process_vars == 4):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][1]])
elif (flag_process_vars == 5):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][1]])
elif (flag_process_vars == 6):
process_vars_d.append(["name_right_array_binary_item_var01", process_vars[i][1][0]])
elif (flag_process_vars == 7):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice"):
if (flag_process_vars == 0):
process_vars_d.append(process_vars[i])
elif (process_vars[i][0] == "name_right_array_binary_slice_var0"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var0", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var0", process_vars[i][1][1]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var0", process_vars[i][1][1]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var0", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice_var1"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var1", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var1", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var1", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var1", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice_var2"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var2", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var2", process_vars[i][1][3]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var2", process_vars[i][1][3]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var2", process_vars[i][1][0]])
elif (flag_process_vars == 3):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice_var01"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 3):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][2]])
elif (flag_process_vars == 4):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][1]])
elif (flag_process_vars == 5):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][1]])
elif (flag_process_vars == 6):
process_vars_d.append(["name_right_array_binary_slice_var01", process_vars[i][1][0]])
elif (flag_process_vars == 7):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice_var02"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][3]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][1]])
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][3]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][3]])
elif (flag_process_vars == 3):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][3]])
elif (flag_process_vars == 4):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][1]])
elif (flag_process_vars == 5):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][1]])
elif (flag_process_vars == 6):
process_vars_d.append(["name_right_array_binary_slice_var02", process_vars[i][1][0]])
elif (flag_process_vars == 7):
pass
elif (process_vars[i][0] == "name_right_array_binary_slice_var12"):
if (flag_process_vars == 0):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][2]])
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][3]])
elif (flag_process_vars == 1):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][2]])
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][3]])
elif (flag_process_vars == 2):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][3]])
elif (flag_process_vars == 3):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][3]])
elif (flag_process_vars == 4):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][0]])
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][2]])
elif (flag_process_vars == 5):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][2]])
elif (flag_process_vars == 6):
process_vars_d.append(["name_right_array_binary_slice_var12", process_vars[i][1][0]])
elif (flag_process_vars == 7):
pass
process_vars = process_vars_d
#----------------------------------------------------------------------------------------------------------------------------------
j = assign_lines_count
for m in range(0, len(process_vars)):
if ((process_vars[m][0] == "name_left") or (process_vars[m][0] == "name_right")):
t = process_vars[m][1].replace('=', '')
t = t.replace(' ', '')
elif (process_vars[m][0] == "name_right_binary_slice"):
t = process_vars[m][1][0]
elif (process_vars[m][0] == "name_right_binary_slice_var0"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_binary_slice_var1"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_binary_slice_var01"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_item"):
t = process_vars[m][1][0]
elif (process_vars[m][0] == "name_right_item_var"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_item"):
t = process_vars[m][1][0]
elif (process_vars[m][0] == "name_right_array_binary_item_var0"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_item_var1"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_item_var01"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice"):
t = process_vars[m][1][0]
elif (process_vars[m][0] == "name_right_array_binary_slice_var0"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice_var1"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice_var2"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice_var01"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice_var02"):
t = process_vars[m][1]
elif (process_vars[m][0] == "name_right_array_binary_slice_var12"):
t = process_vars[m][1]
for i in range (0, len(signals)):
if (t == signals[i]['N']):
if (signals[i]['D'] == 'v'):
L = signals[i]['L'].__doc__
n = signals[i]['N'].__doc__
if (m == 0):
sp = ''
while 1:
if (assign_lines[j][0] == "process_sens_list"):
assign_lines[j][0] = assign_lines[j][0] + "_var"
for k in range(0, assign_lines[j][4]):
sp = sp + ' '
assign_lines[j][1] = assign_lines[j][1].replace("begin", '')
assign_lines[j][1] = assign_lines[j][1] + "\n\n" + sp + "-- Variables"
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "-------------------------------------------------------------------"
if (signals[i]['T'] == 'b'):
if (L.find("int") == 0):
if (n.find("list") == 0):
for k in range(len(signals_intr[i]['N'])):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic;\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic := '" + signals[i]['V'] + "';\n"
elif (n.find("str") == 0):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic;\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic := '" + signals[i]['V'] + "';\n"
elif (L.find("list") == 0):
if (n.find("list") == 0):
for k in range(len(signals[i]['N'])):
if (signals[i].has_key('V') == False):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ");\n"
elif (signals[i].has_key('V') == True):
if (signals_intr[i]['L'][0] > signals_intr[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ") := \"" + signals[i]['V'] + "\";\n"
else:
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ") := '" + signals[i]['V'] + "';\n"
elif (n.find("str") == 0):
if (signals[i].has_key('V') == False):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ");\n"
elif (signals[i].has_key('V') == True):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ") := \"" + signals[i]['V'] + "\";\n"
else:
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ") := '" + signals[i]['V'] + "';\n"
break
elif (signals[i]['T'] == "int"):
if (n.find("str") == 0):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + ";\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + " := " + str(signals[i]['V']) + ";\n"
elif (n.find("list") == 0):
for k in range(len(signals[i]['N'])):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + ";\n"
elif (signals_intr[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'][k] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + " := " + str(signals[i]['V']) + ";\n"
break
elif (signals[i]['T'] == "arrb"):
if (n.find("str") == 0):
if (signals[i]['L'][1][0] > signals[i]['L'][1][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "type type" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of std_logic_vector(" + str(signals_intr[i]['L'][1][0]) + " downto " + str(signals_intr[i]['L'][1][1]) + ");\n"
elif (signals[i]['L'][1][0] < signals[i]['L'][1][1]):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "type type" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of std_logic_vector(" + str(signals_intr[i]['L'][1][0]) + " to " + str(signals_intr[i]['L'][1][1]) + ");\n"
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ";\n"
elif (signals[i].has_key('V') == True):
v = signals[i]['V'].__doc__
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ": \"" + signals[i]['V'] + "\";\n"
elif(v.find("list") == 0):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ": {"
for k in range(0, (signals[i]['L'][0][1] + 1)):
if (k == signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + "\"" + signals[i]['V'][k] + "\"};\n"
elif (k != signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + "\"" + signals[i]['V'][k] + "\", "
count0 = count0 + 1
break
elif (signals[i]['T'] == "arri"):
if (n.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "type type" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of integer range " + str(signals[i]['L'][1][0]) + " to " + str(signals[i]['L'][1][1]) + ";\n"
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ";\n"
elif (signals[i].has_key('V') == True):
v = signals[i]['V'].__doc__
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ": " + str(signals[i]['V']) + ";\n"
elif(v.find("list") == 0):
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "variable " + signals[i]['N'] + ": " + "type" + str(count0) + ": {"
for k in range(0, (signals_intr[i]['L'][0][1] + 1)):
if (k == signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + "};\n"
elif (j != signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + ", "
count0 = count0 + 1
break
elif (signals[i]['T'] == 's'):
v = signals[i]['V'].__doc__
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "type state_type" + str(count1) + " is ("
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'] + ");\n"
elif (v.find("list") == 0):
for k in range(len(signals[i]['V'])):
if (k == (len(signals[i]['V']) - 1)):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + ", "
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "signal " + args[i]['N'] + ": state_type" + str(count1) + ";\n"
count1 = count1 + 1
break
elif (j == 0):
break
j = j - 1
elif (m != 0):
if (signals[i]['T'] == 'b'):
if (L.find("int") == 0):
if (n.find("list") == 0):
for k in range(len(signals_intr[i]['N'])):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic;\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic := '" + signals[i]['V'] + "';\n"
elif (n.find("str") == 0):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic;\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic := '" + signals[i]['V'] + "';\n"
elif (L.find("list") == 0):
if (n.find("list") == 0):
for k in range(len(signals[i]['N'])):
if (signals[i].has_key('V') == False):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ");\n"
elif (signals[i].has_key('V') == True):
if (signals_intr[i]['L'][0] > signals_intr[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ") := \"" + signals[i]['V'] + "\";\n"
else:
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ") := '" + signals[i]['V'] + "';\n"
elif (n.find("str") == 0):
if (signals[i].has_key('V') == False):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ");\n"
elif (signals[i].has_key('V') == True):
if (signals[i]['L'][0] > signals[i]['L'][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " downto " + str(int(signals[i]['L'][1])) + ") := \"" + signals[i]['V'] + "\";\n"
else:
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": std_logic_vector(" + str(int(signals[i]['L'][0])) + " to " + str(int(signals[i]['L'][1])) + ") := '" + signals[i]['V'] + "';\n"
elif (signals[i]['T'] == "int"):
if (n.find("str") == 0):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + ";\n"
elif (signals[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + " := " + str(signals[i]['V']) + ";\n"
elif (n.find("list") == 0):
for k in range(len(signals[i]['N'])):
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + ";\n"
elif (signals_intr[i].has_key('V') == True):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'][k] + ": integer range " + str(signals[i]['L'][0]) + " to " + str(signals[i]['L'][1]) + " := " + str(signals[i]['V']) + ";\n"
elif (signals[i]['T'] == "arrb"):
if (n.find("str") == 0):
if (signals[i]['L'][1][0] > signals[i]['L'][1][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "type typev" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of std_logic_vector(" + str(signals[i]['L'][1][0]) + " downto " + str(signals[i]['L'][1][1]) + ");\n"
elif (signals[i]['L'][1][0] < signals[i]['L'][1][1]):
assign_lines[j][1] = assign_lines[j][1] + sp + "type typev" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of std_logic_vector(" + str(signals_intr[i]['L'][1][0]) + " to " + str(signals_intr[i]['L'][1][1]) + ");\n"
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ";\n"
elif (signals[i].has_key('V') == True):
v = signals[i]['V'].__doc__
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ": \"" + signals[i]['V'] + "\";\n"
elif(v.find("list") == 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ": {"
for k in range(0, (signals[i]['L'][0][1] + 1)):
if (k == signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + "\"" + signals[i]['V'][k] + "\"};\n"
elif (k != signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + "\"" + signals[i]['V'][k] + "\", "
count0 = count0 + 1
elif (signals[i]['T'] == "arri"):
if (n.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "type typev" + str(count0) + " is array (" + str(signals[i]['L'][0][0]) + " to " + str(signals[i]['L'][0][1]) + ") of integer range " + str(signals[i]['L'][1][0]) + " to " + str(signals[i]['L'][1][1]) + ";\n"
if (signals[i].has_key('V') == False):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ";\n"
elif (signals[i].has_key('V') == True):
v = signals[i]['V'].__doc__
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ": " + str(signals[i]['V']) + ";\n"
elif(v.find("list") == 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "variable " + signals[i]['N'] + ": " + "typev" + str(count0) + ": {"
for k in range(0, (signals[i]['L'][0][1] + 1)):
if (k == signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + str(signals[i]['V'][k]) + "};\n"
elif (j != signals[i]['L'][0][1]):
assign_lines[j][1] = assign_lines[j][1] + str(signals[i]['V'][k]) + ", "
count0 = count0 + 1
elif (signals[i]['T'] == 's'):
v = signals[i]['V'].__doc__
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "type state_typev" + str(count1) + " is ("
if (v.find("str") == 0):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'] + ");\n"
elif (v.find("list") == 0):
for k in range(len(signals[i]['V'])):
if (k == (len(signals[i]['V']) - 1)):
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + ");\n"
else:
assign_lines[j][1] = assign_lines[j][1] + signals[i]['V'][k] + ", "
assign_lines[j][1] = assign_lines[j][1] + "\n" + sp + "signal " + args[i]['N'] + ": state_typev" + str(count1) + ";\n"
count1 = count1 + 1
if (len(process_vars) > 0):
assign_lines[j][1] = assign_lines[j][1] + sp + "-------------------------------------------------------------------"
assign_lines[j][1] = assign_lines[j][1] + "\n\n" + sp + "begin\n\n"
| [] |
MaggieIllustrations/softuni-github-programming | Giraffe/Functions.py | f5695cb14602f3d2974359f6d8734332acc650d3 | def say_hi(name,age):
print("Hello " + name + ", you are " + age)
say_hi("Mike", "35")
def cube(num): # function
return num*num*num
result = cube(4) # variable
print(result)
| [] |
wipfli/airspaces | airspace_surgery.py | c2e01615fa6a065895ed04b8f342a38732e9196b | import glob
import json
path_in = './airspaces/'
path_out = './airspaces_processed/'
filenames = [path.split('/')[-1] for path in glob.glob(path_in + '*')]
remove = {
'france_fr.geojson': [
314327,
314187,
314360,
314359,
314362,
314361,
314364,
314363,
314333,
314329,
314331,
],
'germany_de.geojson': [
307563,
307638,
307639,
307640,
]
}
replacements = {
'france_fr.geojson': [
['Bale10 119.35', 'Bale 10 TMA 130.9'],
['Bale1 119.35', 'Bale 1 TMA 130.9'],
['Bale2 119.35', 'Bale 2 TMA 130.9'],
['Bale3 119.35', 'Bale 3 TMA 130.9'],
['Bale4 119.35', 'Bale 4 TMA 130.9'],
['Bale5 119.35', 'Bale 5 TMA 130.9'],
['Bale5 119.35', 'Bale 5 TMA 130.9'],
['Bale6 119.35', 'Bale 6 TMA 130.9'],
['Bale7 119.35', 'Bale 7 TMA 130.9'],
['Bale8 119.35', 'Bale 8 TMA 130.9'],
['Bale9 119.35', 'Bale 9 TMA 130.9'],
['Bale AZ4T1 134.67', 'Bale T1 TMA HX 134.68'],
['Bale AZ4T2 134.67', 'Bale T2 TMA HX 134.68'],
['Bale AZ4T3 134.67', 'Bale T3 TMA HX 134.68'],
['CTR BALE', 'Bale CTR 118.3']
],
'switzerland_ch.geojson': [
['ZURICH 10 TMA 118.1', 'ZURICH 10 TMA 124.7'],
['ZURICH 11 TMA 118.1', 'ZURICH 11 TMA 124.7'],
['ZURICH 12 TMA 118.1', 'ZURICH 12 TMA 124.7'],
['ZURICH 13 TMA 118.1', 'ZURICH 13 TMA 124.7'],
['ZURICH 14 TMA 118.1', 'ZURICH 14 TMA HX 127.755'],
['ZURICH 15 TMA 118.1', 'ZURICH 15 TMA HX 127.755'],
['ZURICH 1 TMA 118.1', 'ZURICH 1 TMA 124.7'],
['ZURICH 2 CTR 118.1', 'ZURICH 2 CTR HX 118.975'],
['ZURICH 2 TMA 118.1', 'ZURICH 2 TMA 124.7'],
['ZURICH 3 TMA 118.1', 'ZURICH 3 TMA 124.7'],
['ZURICH 4A TMA 118.1', 'ZURICH 4A TMA 124.7'],
['ZURICH 4B TMA 118.1', 'ZURICH 4B TMA 124.7'],
['ZURICH 4C TMA 118.1', 'ZURICH 4C TMA 124.7'],
['ZURICH 5 TMA 118.1', 'ZURICH 5 TMA 124.7'],
['ZURICH 6 TMA 118.1', 'ZURICH 6 TMA 124.7'],
['ZURICH 7 TMA 118.1', 'ZURICH 7 TMA 124.7'],
['ZURICH 8 TMA 118.1', 'ZURICH 8 TMA 124.7'],
['ZURICH 9 TMA 118.1', 'ZURICH 9 TMA 124.7'],
['BERN 1 TMA 121.025', 'BERN 1 TMA HX 127.325'],
['BERN 2 TMA 121.025', 'BERN 2 TMA HX 127.325'],
['BERN CTR 121.025', 'BERN CTR HX 121.025'],
['EMMEN 1 CTR 120.425', 'EMMEN 1 CTR HX 120.425'],
['EMMEN 1 TMA 120.425', 'EMMEN 1 TMA HX 134.130'],
['EMMEN 2 CTR 120.425', 'EMMEN 2 CTR HX 120.425'],
['EMMEN 2 TMA 120.425', 'EMMEN 2 TMA HX 134.130'],
['EMMEN 3 TMA 120.425', 'EMMEN 3 TMA HX 134.130'],
['EMMEN 4 TMA 120.425', 'EMMEN 4 TMA HX 134.130'],
['EMMEN 5 TMA 120.425', 'EMMEN 5 TMA HX 134.130'],
['EMMEN 6 TMA 120.425', 'EMMEN 6 TMA HX 134.130'],
]
}
for filename in filenames:
print(filename)
with open(path_in + filename) as f:
data = json.load(f)
if filename in replacements:
targets = [r[0] for r in replacements[filename]]
for feature in data['features']:
if feature['properties']['N'] in targets:
print('replace ' + feature['properties']['N'] + '...')
feature['properties']['N'] = next(x for x in replacements[filename] if x[0] == feature['properties']['N'])[1]
if filename in remove:
features_out = [f for f in data['features'] if int(f['properties']['ID']) not in remove[filename]]
else:
features_out = data['features']
print('removed ' + str(len(data['features']) - len(features_out)) + ' features')
geojson = {
'type': 'FeatureCollection',
'features': features_out
}
print('write ' + filename + '...')
with open(path_out + filename, 'w') as f:
json.dump(geojson, f)
all_features = []
for filename in filenames:
print('read ' + filename + '...')
with open(path_out + filename) as f:
all_features += json.load(f)['features']
print('write airspaces.geojson...')
with open('airspaces.geojson', 'w') as f:
json.dump({
'type': 'FeatureCollection',
'features': all_features
}, f)
print('done')
| [((122, 4, 125, 9), 'json.dump', 'json.dump', ({(122, 14, 125, 5): "{'type': 'FeatureCollection', 'features': all_features}", (125, 7, 125, 8): 'f'}, {}), "({'type': 'FeatureCollection', 'features': all_features}, f)", False, 'import json\n'), ((7, 45, 7, 69), 'glob.glob', 'glob.glob', ({(7, 55, 7, 68): "(path_in + '*')"}, {}), "(path_in + '*')", False, 'import glob\n'), ((89, 15, 89, 27), 'json.load', 'json.load', ({(89, 25, 89, 26): 'f'}, {}), '(f)', False, 'import json\n'), ((111, 8, 111, 29), 'json.dump', 'json.dump', ({(111, 18, 111, 25): 'geojson', (111, 27, 111, 28): 'f'}, {}), '(geojson, f)', False, 'import json\n'), ((117, 24, 117, 36), 'json.load', 'json.load', ({(117, 34, 117, 35): 'f'}, {}), '(f)', False, 'import json\n')] |
lidenghong1/SmallReptileTraining | AndroidSpider/spider_main.py | a1bfb81c9969edfb7554acc50370c0cb036da690 | from AndroidSpider import url_manager, html_downloader, html_parser, html_output
'''
爬取百度百科 Android 关键词相关词及简介并输出为一个HTML tab网页
Extra module:
BeautifulSoup
'''
class SpiderMain(object):
def __init__(self):
self.urls = url_manager.UrlManager()
self.downloader = html_downloader.HtmlDownLoader()
self.parser = html_parser.HtmlParser()
self.out_put = html_output.HtmlOutput()
def craw(self, root_url):
count = 1
self.urls.add_new_url(root_url)
while self.urls.has_new_url():
try:
new_url = self.urls.get_new_url()
print("craw %d : %s" % (count, new_url))
headers = {
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36"
}
html_content = self.downloader.download(new_url, retry_count=2, headers=headers)
new_urls, new_data = self.parser.parse(new_url, html_content, "utf-8")
self.urls.add_new_urls(new_urls)
self.out_put.collect_data(new_data)
if count >= 30:
break
count = count + 1
except Exception as e:
print("craw failed!\n"+str(e))
self.out_put.output_html()
if __name__ == "__main__":
rootUrl = "http://baike.baidu.com/item/Android"
objSpider = SpiderMain()
objSpider.craw(rootUrl)
| [((11, 20, 11, 44), 'AndroidSpider.url_manager.UrlManager', 'url_manager.UrlManager', ({}, {}), '()', False, 'from AndroidSpider import url_manager, html_downloader, html_parser, html_output\n'), ((12, 26, 12, 58), 'AndroidSpider.html_downloader.HtmlDownLoader', 'html_downloader.HtmlDownLoader', ({}, {}), '()', False, 'from AndroidSpider import url_manager, html_downloader, html_parser, html_output\n'), ((13, 22, 13, 46), 'AndroidSpider.html_parser.HtmlParser', 'html_parser.HtmlParser', ({}, {}), '()', False, 'from AndroidSpider import url_manager, html_downloader, html_parser, html_output\n'), ((14, 23, 14, 47), 'AndroidSpider.html_output.HtmlOutput', 'html_output.HtmlOutput', ({}, {}), '()', False, 'from AndroidSpider import url_manager, html_downloader, html_parser, html_output\n')] |
trompamusic/ce-queries-template | trompace/mutations/__init__.py | cc5ae69d0e76623bfd72e9453f569f6624bf7c3b | MUTATION = '''mutation {{
{mutation}
}}'''
def _verify_additional_type(additionaltype):
"""Check that the input to additionaltype is a list of strings.
If it is empty, raise ValueError
If it is a string, convert it to a list of strings."""
if additionaltype is None:
return None
if isinstance(additionaltype, str):
additionaltype = [additionaltype]
if len(additionaltype) == 0:
raise ValueError("additionaltype must be a non-empty list")
return additionaltype
| [] |
CapitalOneDevExchangeHackathon/Financial-Fitness | Web_App/infrastructure/infra.py | 54a2203d6b3d96687d822247b040613b644874f2 | import boto
import boto3
from config import Config
dynamodb = boto3.resource('dynamodb',
aws_access_key_id=Config.AWS_KEY,
aws_secret_access_key=Config.AWS_SECRET_KEY,
region_name=Config.REGION)
table = dynamodb.Table('user_details')
tables = boto3.resource('dynamodb', aws_access_key_id=Config.AWS_KEY,
aws_secret_access_key=Config.AWS_SECRET_KEY, region_name=Config.REGION).Table('user_details')
print(tables.creation_date_time)
def main():
print("29.7604267")
def insert_into_db(user):
print(user.lastname)
try:
table.put_item(
Item={
'pin': user.pin,
'firstname': user.firstname,
'lastname': user.lastname,
}
)
except Exception as E:
print(E)
return False
return True
if __name__ == "__main__":
main()
| [((5, 11, 8, 52), 'boto3.resource', 'boto3.resource', (), '', False, 'import boto3\n'), ((11, 9, 12, 97), 'boto3.resource', 'boto3.resource', (), '', False, 'import boto3\n')] |
ndarwin314/symbolicPy | numberTheory/natural.py | ce2e48bf1557b5995db6c324ada9fbd4767df1e3 | # TODO: implement algorithms in c++ or something to make them fast
| [] |
TeaPackCZ/RobotZed | SelfTests.py | 7ac8bfb14a6c2e5887f8fed299ad87b384701c54 | import os
import unittest
from Logger import Logger
class TestLogger(unittest.TestCase):
def test_file_handling(self):
testLog = Logger("testLog")
## Check if program can create and open file
self.assertTrue(testLog.opened)
returns = testLog.close()
## Check if logger correctly signs bool OPENED and returns
## 0 as succes.
self.assertFalse(testLog.opened)
self.assertEqual(returns,0)
returns = testLog.close()
## Check if logger returns 1 when trying to close already
## closed file
self.assertEqual(returns,1)
## Do cleanup:
os.remove(testLog.name)
def test_logging(self):
testLog = Logger("testLog")
testPhrase = "TestLine\r\n"
testLog.save_line(testPhrase)
testLog.close()
logfile = open(testLog.name)
content = logfile.read()
logfile.close()
saved = content.split(" : ")
## Check if saved data corresponds
self.assertEqual(saved[1],testPhrase)
## cleanup
os.remove(testLog.name)
from gpsNavigation import gpsModule,gpsPoint
class TestGPSNavigation(unittest.TestCase):
def test_gps_angles(self):
gpsMod = gpsModule()
A = gpsPoint(10,10)
B = gpsPoint(10.1,10.1)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,15623.0)
self.assertEqual(azimut,45.0)
B = gpsPoint(10.0,10.1)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,10963.0)
self.assertEqual(azimut,90.0)
B = gpsPoint(9.9,10.1)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,15625.0)
self.assertEqual(azimut,135.0)
B = gpsPoint(9.9,10.0)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,11132.0)
self.assertEqual(azimut,180.0)
B = gpsPoint(9.9,9.9)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,15625.0)
self.assertEqual(azimut,225.0)
B = gpsPoint(10.0,9.9)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,10963.0)
self.assertEqual(azimut,270.0)
B = gpsPoint(10.1,9.9)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,15623.0)
self.assertEqual(azimut,315.0)
B = gpsPoint(10.1,10.0)
distance, azimut = gpsMod.GPSData.getDirAndDist(A,B)
self.assertEqual(distance,11132.0)
self.assertEqual(azimut,0)
if __name__ == '__main__':
unittest.main()
| [((85, 4, 85, 19), 'unittest.main', 'unittest.main', ({}, {}), '()', False, 'import unittest\n'), ((7, 18, 7, 35), 'Logger.Logger', 'Logger', ({(7, 25, 7, 34): '"""testLog"""'}, {}), "('testLog')", False, 'from Logger import Logger\n'), ((20, 8, 20, 31), 'os.remove', 'os.remove', ({(20, 18, 20, 30): 'testLog.name'}, {}), '(testLog.name)', False, 'import os\n'), ((23, 18, 23, 35), 'Logger.Logger', 'Logger', ({(23, 25, 23, 34): '"""testLog"""'}, {}), "('testLog')", False, 'from Logger import Logger\n'), ((34, 8, 34, 31), 'os.remove', 'os.remove', ({(34, 18, 34, 30): 'testLog.name'}, {}), '(testLog.name)', False, 'import os\n'), ((39, 17, 39, 28), 'gpsNavigation.gpsModule', 'gpsModule', ({}, {}), '()', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((41, 12, 41, 27), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(41, 21, 41, 23): '10', (41, 24, 41, 26): '10'}, {}), '(10, 10)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((42, 12, 42, 31), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(42, 21, 42, 25): '10.1', (42, 26, 42, 30): '10.1'}, {}), '(10.1, 10.1)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((48, 12, 48, 31), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(48, 21, 48, 25): '10.0', (48, 26, 48, 30): '10.1'}, {}), '(10.0, 10.1)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((53, 12, 53, 30), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(53, 21, 53, 24): '9.9', (53, 25, 53, 29): '10.1'}, {}), '(9.9, 10.1)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((58, 12, 58, 30), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(58, 21, 58, 24): '9.9', (58, 25, 58, 29): '10.0'}, {}), '(9.9, 10.0)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((63, 12, 63, 29), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(63, 21, 63, 24): '9.9', (63, 25, 63, 28): '9.9'}, {}), '(9.9, 9.9)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((68, 12, 68, 30), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(68, 21, 68, 25): '10.0', (68, 26, 68, 29): '9.9'}, {}), '(10.0, 9.9)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((73, 12, 73, 30), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(73, 21, 73, 25): '10.1', (73, 26, 73, 29): '9.9'}, {}), '(10.1, 9.9)', False, 'from gpsNavigation import gpsModule, gpsPoint\n'), ((78, 12, 78, 31), 'gpsNavigation.gpsPoint', 'gpsPoint', ({(78, 21, 78, 25): '10.1', (78, 26, 78, 30): '10.0'}, {}), '(10.1, 10.0)', False, 'from gpsNavigation import gpsModule, gpsPoint\n')] |
Abijithkrishna/manga-py | manga_py/parser.py | 03b142ecb944ef37a36e5095ffa580209021e3b0 | from logging import warning
from requests import get
from .info import Info
from .provider import Provider
from .providers import get_provider
class Parser:
def __init__(self, args: dict):
self.params = args
def init_provider(
self,
chapter_progress: callable = None,
global_progress: callable = None,
log: callable = None,
quest: callable = None,
info: Info = None,
quest_password: callable = None,
):
original_url = self.params.get('url', '')
provider_url = self.params.get('force_provider', None)
provider = get_provider(provider_url or original_url)
if isinstance(provider, bool):
raise AttributeError('Provider not found')
# update url (if redirect)
self.provider = provider(info) # type: Provider
self.provider.original_url = original_url
real_url = self.check_url(original_url)
if self.provider.allow_auto_change_url():
if real_url != original_url:
warning('Manga url changed! New url: {}'.format(real_url))
self.params['url'] = real_url
self.provider.quiet = self.params.get('quiet', False)
self.provider.set_chapter_progress_callback(chapter_progress)
self.provider.set_global_progress_callback(global_progress)
self.provider.set_log_callback(log)
self.provider.set_quest_callback(quest)
self.provider.set_quest_password_callback(quest_password)
def start(self):
self.provider.process(self.params['url'], self.params)
def check_url(self, url):
proxy = self.params.get('proxy', None)
proxies = {
'http': proxy,
'https': proxy,
} if proxy else None
with get(url, stream=True, proxies=proxies) as response:
_url = response.url
if url != _url:
url = _url
return url
| [((62, 13, 62, 51), 'requests.get', 'get', (), '', False, 'from requests import get\n')] |
pwelzel/bornhack-website | src/villages/migrations/0008_auto_20161228_2209.py | af794e6a2fba06e09626259c7768feb30ff394be | # -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2016-12-28 22:09
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('villages', '0007_village_camp'),
]
operations = [
migrations.AlterField(
model_name='village',
name='camp',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='camps.Camp'),
),
]
| [((19, 18, 19, 97), 'django.db.models.ForeignKey', 'models.ForeignKey', (), '', False, 'from django.db import migrations, models\n')] |
sindhumadhadi09/CustomerMgmt | customers/views.py | db8b27ad6ceb8050843dc33509dc2b6c2ed2c1e2 | from django.shortcuts import get_object_or_404, render
from django.http import HttpResponseRedirect
from django.urls import reverse
from django.views import generic
from django.utils import timezone
from .models import Customer
class IndexView(generic.ListView):
template_name = 'customers/index.html'
context_object_name = 'customers_list'
def get_queryset(self):
return Customer.objects.all()
class CustomerView(generic.TemplateView):
template_name = 'customers/detail.html'
def add_customer(request):
customer = Customer()
customer.customer_firstname = request.POST['fname']
customer.customer_lastname = request.POST['lname']
customer.customer_address = request.POST['address']
customer.customer_city = request.POST['city']
customer.customer_zipcode = request.POST['zip']
customer.customer_state = request.POST['state']
customer.save()
return HttpResponseRedirect(reverse('customers:index'))
def delete_customer(request, customer_id):
p = Customer.objects.get(pk=customer_id)
p.delete()
return HttpResponseRedirect(reverse('customers:index')) | [((28, 32, 28, 58), 'django.urls.reverse', 'reverse', ({(28, 40, 28, 57): '"""customers:index"""'}, {}), "('customers:index')", False, 'from django.urls import reverse\n'), ((33, 32, 33, 58), 'django.urls.reverse', 'reverse', ({(33, 40, 33, 57): '"""customers:index"""'}, {}), "('customers:index')", False, 'from django.urls import reverse\n')] |
yuriks/salt | salt/ext/tornado/test/import_test.py | d2a5bd8adddb98ec1718d79384aa13b4f37e8028 | # flake8: noqa
# pylint: skip-file
from __future__ import absolute_import, division, print_function
from salt.ext.tornado.test.util import unittest
class ImportTest(unittest.TestCase):
def test_import_everything(self):
# Some of our modules are not otherwise tested. Import them
# all (unless they have external dependencies) here to at
# least ensure that there are no syntax errors.
import tornado.auth
import tornado.autoreload
import tornado.concurrent
import tornado.escape
import tornado.gen
import tornado.http1connection
import tornado.httpclient
import tornado.httpserver
import tornado.httputil
import tornado.ioloop
import tornado.iostream
import tornado.locale
import tornado.log
import tornado.netutil
import tornado.options
import tornado.process
import tornado.simple_httpclient
import tornado.stack_context
import tornado.tcpserver
import tornado.tcpclient
import tornado.template
import tornado.testing
import tornado.util
import tornado.web
import tornado.websocket
import tornado.wsgi
# for modules with dependencies, if those dependencies can be loaded,
# load them too.
def test_import_pycurl(self):
try:
import pycurl # type: ignore
except ImportError:
pass
else:
import tornado.curl_httpclient
| [] |
fossabot/butterfree | butterfree/configs/db/metastore_config.py | 8a7da8c540b51c6560b2825cb926c40a351f202b | """Holds configurations to read and write with Spark to AWS S3."""
import os
from typing import Any, Dict, List, Optional
from pyspark.sql import DataFrame
from butterfree.configs import environment
from butterfree.configs.db import AbstractWriteConfig
from butterfree.dataframe_service import extract_partition_values
class MetastoreConfig(AbstractWriteConfig):
"""Configuration for Spark metastore database stored.
By default the configuration is for AWS S3.
Attributes:
path: database root location.
mode: writing mode used be writers.
format_: expected stored file format.
file_system: file schema uri, like: s3a, file.
"""
def __init__(
self,
path: str = None,
mode: str = None,
format_: str = None,
file_system: str = None,
):
self.path = path
self.mode = mode
self.format_ = format_
self.file_system = file_system
@property
def path(self) -> Optional[str]:
"""Bucket name."""
return self.__path
@path.setter
def path(self, value: str) -> None:
self.__path = value or environment.get_variable("FEATURE_STORE_S3_BUCKET")
@property
def format_(self) -> Optional[str]:
"""Expected stored file format."""
return self.__format
@format_.setter
def format_(self, value: str) -> None:
self.__format = value or "parquet"
@property
def mode(self) -> Optional[str]:
"""Writing mode used be writers."""
return self.__mode
@mode.setter
def mode(self, value: str) -> None:
self.__mode = value or "overwrite"
@property
def file_system(self) -> Optional[str]:
"""Writing mode used be writers."""
return self.__file_system
@file_system.setter
def file_system(self, value: str) -> None:
self.__file_system = value or "s3a"
def get_options(self, key: str) -> Dict[Optional[str], Optional[str]]:
"""Get options for Metastore.
Options will be a dictionary with the write and read configuration for
Spark Metastore.
Args:
key: path to save data into Metastore.
Returns:
Options configuration for Metastore.
"""
return {
"mode": self.mode,
"format_": self.format_,
"path": os.path.join(f"{self.file_system}://{self.path}/", key),
}
def get_path_with_partitions(self, key: str, dataframe: DataFrame) -> List:
"""Get options for AWS S3 from partitioned parquet file.
Options will be a dictionary with the write and read configuration for
Spark to AWS S3.
Args:
key: path to save data into AWS S3 bucket.
dataframe: spark dataframe containing data from a feature set.
Returns:
A list of string for file-system backed data sources.
"""
path_list = []
dataframe_values = extract_partition_values(
dataframe, partition_columns=["year", "month", "day"]
)
for row in dataframe_values:
path_list.append(
f"{self.file_system}://{self.path}/{key}/year={row['year']}/"
f"month={row['month']}/day={row['day']}"
)
return path_list
def translate(self, schema: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Translate feature set spark schema to the corresponding database."""
pass
| [((107, 27, 109, 9), 'butterfree.dataframe_service.extract_partition_values', 'extract_partition_values', (), '', False, 'from butterfree.dataframe_service import extract_partition_values\n'), ((45, 31, 45, 82), 'butterfree.configs.environment.get_variable', 'environment.get_variable', ({(45, 56, 45, 81): '"""FEATURE_STORE_S3_BUCKET"""'}, {}), "('FEATURE_STORE_S3_BUCKET')", False, 'from butterfree.configs import environment\n'), ((90, 20, 90, 75), 'os.path.join', 'os.path.join', ({(90, 33, 90, 69): 'f"""{self.file_system}://{self.path}/"""', (90, 71, 90, 74): 'key'}, {}), "(f'{self.file_system}://{self.path}/', key)", False, 'import os\n')] |
johanngan/special_relativity | examples/2-objects.py | cd372c7460d2c0d4040c81bc1bd0090086dba735 | #!/usr/bin/env python3
import sys
sys.path.append('..')
import specrel.geom as geom
import specrel.spacetime.physical as phy
import specrel.visualize as vis
# Shared parameters
include_grid = True
include_legend = True
tlim = (0, 2)
xlim = (-2, 2)
# A stationary point object
stationary = phy.MovingObject(0, draw_options={'label': '$v = 0$'})
## Alternate:
# direction = (1, 0)
# point = (0, 0)
# stationary = geom.Line(direction, point, draw_options={'label': '$v = 0$'})
title='Stationary object'
p = vis.stplot(stationary, title=title, tlim=tlim, xlim=xlim,
grid=include_grid, legend=include_legend)
p.save('2-objects_stationary_point.png')
p.show()
# A stationary point object, animated
anim = vis.stanimate(stationary, title=title, tlim=tlim, xlim=xlim,
grid=include_grid, legend=include_legend)
anim.save('2-objects_stationary_point_anim.mp4')
anim.show()
# A stationary point object, animated with worldline
anim = vis.stanimate_with_worldline(stationary, title=title,
tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend,
legend_loc='upper right')
anim.save('2-objects_stationary_point_anim_worldline.mp4')
anim.show()
# A bunch of moving point objects, animated
moving = phy.MovingObject(0, velocity=1/2,
draw_options={'color': 'red', 'label': '$v = c/2$'})
light = phy.MovingObject(0, velocity=1,
draw_options={'color': 'gold', 'label': '$v = c$'})
ftl = phy.MovingObject(0, velocity=3/2,
draw_options={'color': 'cyan', 'label': '$v = 3c/2$'})
objects = geom.Collection([stationary, moving, light, ftl])
title = 'Various objects'
anim = vis.stanimate_with_worldline(objects, title=title,
current_time_color='magenta', tlim=tlim, xlim=xlim, grid=include_grid,
legend=include_legend, legend_loc='upper left')
anim.save('2-objects_moving_points.mp4')
anim.show()
# A moving meterstick
meterstick = phy.MovingObject(-1/2, length=1, velocity=1/2,
draw_options={'label': 'Meterstick'})
# # Alternate:
# direction = (1, 1/2)
# left = geom.Line(direction, (0, -1/2))
# right = geom.Line(direction, (0, 1/2))
# meterstick = geom.Ribbon(left, right, draw_options={'label': 'Meterstick'})
title = 'Moving meterstick ($v = c/2$)'
anim = vis.stanimate_with_worldline(meterstick, title=title,
tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend,
legend_loc='upper left')
anim.save('2-objects_moving_meterstick.mp4')
anim.show()
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mfkiwl/OpenXcvr | firmware/modulator.py | 9bea6efd03cd246f16982f0fadafed684ac5ce1c | from baremetal import *
from math import pi, sin, cos
import sys
from scale import scale
from settings import *
from ssb import ssb_polar
def modulator(clk, audio, audio_stb, settings):
audio_bits = audio.subtype.bits
#AM modulation
am_mag = Unsigned(12).constant(0) + audio + 2048
am_phase = Signed(32).constant(0)
am_stb = audio_stb
#FM modulation
fm_mag = Unsigned(12).constant(4095)
frequency = Signed(32).constant(0) + audio
nfm_scaled_frequency = frequency * (2**(32-audio_bits) * 5 / 50)
nfm_phase = nfm_scaled_frequency.subtype.register(clk, en=audio_stb, init=0)
nfm_phase.d(nfm_phase + nfm_scaled_frequency)
scaled_frequency = frequency * (2**(32-audio_bits) * 8 / 50)
fm_phase = scaled_frequency.subtype.register(clk, en=audio_stb, init=0)
fm_phase.d(fm_phase + scaled_frequency)
fm_stb = Boolean().register(clk, d=audio_stb, init=0)
#ssb
ssb_mag, ssb_phase, ssb_stb = ssb_polar(clk, audio, audio_stb, settings.mode==LSB)
ssb_mag <<= 1
ssb_phase = Signed(32).constant(0) + ssb_phase
ssb_phase <<= (32 - audio_bits)
#cw modulation
cw_mag = Unsigned(12).constant(0)
cw_phase = Signed(32).constant(0)
cw_stb = audio_stb
#mode switching
magnitude = Unsigned(12).select(settings.mode, am_mag, fm_mag, fm_mag, ssb_mag, ssb_mag, cw_mag)
phase = Signed(32).select(settings.mode, am_phase, nfm_phase, fm_phase, ssb_phase, ssb_phase, cw_phase)
stb = Boolean().select(settings.mode, am_stb, fm_stb, fm_stb, ssb_stb, ssb_stb, cw_stb)
return magnitude, phase, audio_stb
import numpy as np
from matplotlib import pyplot as plt
def test_modulator(stimulus, mode):
settings = Settings()
settings.mode = Unsigned(3).input("filter_mode")
clk = Clock("clk")
audio_in = Signed(12).input("i_data_in")
audio_stb_in = Boolean().input("stb_in")
i, q, stb = modulator(clk, audio_in, audio_stb_in, settings)
#simulate
clk.initialise()
settings.mode.set(mode)
response = []
for data in stimulus:
for j in range(200):
audio_stb_in.set(j==199)
audio_in.set(data)
clk.tick()
if stb.get():
print i.get(), q.get()
if i.get() is None or q.get() is None:
continue
response.append(i.get()*(2**20)+1j*q.get())
response = np.array(response)
plt.title("Modulator")
plt.xlabel("Time (samples)")
plt.ylabel("Value")
a, = plt.plot(np.real(response), label="I")
b, = plt.plot(np.imag(response), label="Q")
c, = plt.plot(stimulus*(2**20), label="Audio Input")
plt.legend(handles=[a, b, c])
plt.show()
if __name__ == "__main__" and "sim" in sys.argv:
#mode am stim am
stimulus=(
np.sin(np.arange(1000)*2.0*pi*0.02)*1023+
np.sin(np.arange(1000)*2.0*pi*0.03)*1023
)
#test_modulator(stimulus, FM)
#test_modulator(stimulus, FM)
#test_modulator(stimulus, NBFM)
test_modulator(stimulus, USB)
| [] |
pierredup/sentry | tests/sentry/auth/test_helper.py | 0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80 | from __future__ import absolute_import
from six.moves.urllib.parse import urlencode
from django.test import RequestFactory
from django.contrib.auth.models import AnonymousUser
from sentry.auth.helper import handle_new_user
from sentry.models import AuthProvider, InviteStatus, OrganizationMember
from sentry.testutils import TestCase
from sentry.utils.compat import mock
class HandleNewUserTest(TestCase):
@mock.patch("sentry.analytics.record")
def test_simple(self, mock_record):
provider = "dummy"
request = RequestFactory().post("/auth/sso/")
request.user = AnonymousUser()
auth_provider = AuthProvider.objects.create(
organization=self.organization, provider=provider
)
identity = {"id": "1234", "email": "[email protected]", "name": "Morty"}
auth_identity = handle_new_user(auth_provider, self.organization, request, identity)
user = auth_identity.user
assert user.email == identity["email"]
assert OrganizationMember.objects.filter(organization=self.organization, user=user).exists()
signup_record = [r for r in mock_record.call_args_list if r[0][0] == "user.signup"]
assert signup_record == [
mock.call(
"user.signup", user_id=user.id, source="sso", provider=provider, referrer="in-app"
)
]
def test_associated_existing_member_invite_by_email(self):
request = RequestFactory().post("/auth/sso/")
request.user = AnonymousUser()
provider = AuthProvider.objects.create(organization=self.organization, provider="dummy")
identity = {"id": "1234", "email": "[email protected]", "name": "Morty"}
member = OrganizationMember.objects.create(
organization=self.organization, email=identity["email"]
)
auth_identity = handle_new_user(provider, self.organization, request, identity)
assigned_member = OrganizationMember.objects.get(
organization=self.organization, user=auth_identity.user
)
assert assigned_member.id == member.id
def test_associated_existing_member_invite_request(self):
request = RequestFactory().post("/auth/sso/")
request.user = AnonymousUser()
provider = AuthProvider.objects.create(organization=self.organization, provider="dummy")
identity = {"id": "1234", "email": "[email protected]", "name": "Morty"}
member = self.create_member(
organization=self.organization,
email=identity["email"],
invite_status=InviteStatus.REQUESTED_TO_BE_INVITED.value,
)
auth_identity = handle_new_user(provider, self.organization, request, identity)
assert OrganizationMember.objects.filter(
organization=self.organization,
user=auth_identity.user,
invite_status=InviteStatus.APPROVED.value,
).exists()
assert not OrganizationMember.objects.filter(id=member.id).exists()
def test_associate_pending_invite(self):
provider = AuthProvider.objects.create(organization=self.organization, provider="dummy")
identity = {"id": "1234", "email": "[email protected]", "name": "Morty"}
# The org member invite should have a non matching email, but the
# member id and token will match from the cookie, allowing association
member = OrganizationMember.objects.create(
organization=self.organization, email="[email protected]", token="abc"
)
request = RequestFactory().post("/auth/sso/")
request.user = AnonymousUser()
request.COOKIES["pending-invite"] = urlencode(
{"memberId": member.id, "token": member.token, "url": ""}
)
auth_identity = handle_new_user(provider, self.organization, request, identity)
assigned_member = OrganizationMember.objects.get(
organization=self.organization, user=auth_identity.user
)
assert assigned_member.id == member.id
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richo/groundstation | groundstation/broadcast_events/__init__.py | 7ed48dd355051ee6b71164fc801e3893c09d11db | from broadcast_ping import BroadcastPing
EVENT_TYPES = {
"PING": BroadcastPing,
}
class UnknownBroadcastEvent(Exception):
pass
def new_broadcast_event(data):
event_type, payload = data.split(" ", 1)
if event_type not in EVENT_TYPES:
raise UnknownBroadcastEvent(event_type)
return EVENT_TYPES[event_type](payload)
| [] |
dougzor/mbta_python | mbta_python/__init__.py | f277f48f8bf8048cb5c9c6307e672c37292e57f7 | import datetime
import requests
from mbta_python.models import Stop, Direction, Schedule, Mode, \
TripSchedule, Alert, StopWithMode, Prediction
HOST = "http://realtime.mbta.com/developer/api/v2"
def datetime_to_epoch(dt):
epoch = datetime.datetime.utcfromtimestamp(0)
return int((dt - epoch).total_seconds())
class MBTASDK(object):
"""Wrapper around calls to the MBTA Realtime API
"""
def __init__(self, api_key):
self.api_key = api_key
def _make_request(self, path, params):
url = "{}/{}".format(HOST, path)
response = requests.get(url, params=params)
data = response.json()
error = data.get("error")
if error:
raise Exception(error["message"])
return response.json()
def get_stops_by_location(self, latitude, longitude):
"""Get a List of Stops sorted by proximity to the given
latitude and longitude
"""
params = {
"lat": latitude,
"lon": longitude,
"api_key": self.api_key,
"format": "json"
}
data = self._make_request("stopsbylocation", params)
stops = [Stop(stop_data) for stop_data in data["stop"]]
return stops
def get_stops_by_route(self, route_id):
"""Return a List of Directions for the route_id
that contain a list of Stops that Direction and Route serve
"""
params = {
"route": route_id,
"api_key": self.api_key,
"format": "json"
}
data = self._make_request("stopsbyroute", params)
return [Direction(d) for d in data["direction"]]
def get_routes_by_stop(self, stop_id):
"""Return a list of routes that serve a particular stop
"""
params = {
"stop": stop_id,
"api_key": self.api_key,
"format": "json"
}
data = self._make_request("routesbystop", params)
return StopWithMode(data)
def get_schedules_by_stop(self, stop_id, route_id=None, direction_id=None,
date=None, max_time=None, max_trips=None):
"""Return scheduled arrivals and departures for a direction and route for a
particular stop.
stop_id - Stop ID
route_id - Route ID, If not included then schedule for all routes
serving the stop will be returned,
direction_id - Direction ID, If included then route must also be
included if not included then schedule for all
directions of the route serving the stop will be
returned
date - Time after which schedule should be returned. If included
then must be within the next seven (7) days
If not included then schedule starting from the current
datetime will be returned
max_time - Defines maximum range of time (in minutes) within which
trips will be returned. If not included defaults to 60.
max_trips - Defines number of trips to return. Integer between 1 and
100. If not included defaults to 5.
"""
params = {
"stop": stop_id,
"api_key": self.api_key,
"format": "json",
"route": route_id,
"direction": direction_id,
"datetime": datetime_to_epoch(date) if date else None,
"max_time": max_time,
"max_trips": max_trips
}
data = self._make_request("schedulebystop", params)
return Schedule(data)
def get_schedules_by_routes(self, route_ids, date=None,
max_time=None, max_trips=None):
"""Return the scheduled arrivals and departures in a direction
for a particular route or routes.
route_ids - List of Route IDs, or single Route ID
date - Time after which schedule should be returned. If included
then must be within the next seven (7) days If not included
then schedule starting from the current datetime will
be returned
max_time - Defines maximum range of time (in minutes) within which
trips will be returned. If not included defaults to 60.
max_trips - Defines number of trips to return. Integer between 1
and 100. If not included defaults to 5.
"""
if not isinstance(route_ids, list):
route_ids = [route_ids]
params = {
"routes": ",".join(route_ids),
"api_key": self.api_key,
"format": "json",
"datetime": datetime_to_epoch(date) if date else None,
"max_time": max_time,
"max_trips": max_trips
}
data = self._make_request("schedulebyroutes", params)
return [Mode(m) for m in data["mode"]]
def get_schedules_by_trip(self, trip_id, date=None):
"""Return the scheduled arrivals and departures in a direction
for a particular route or routes.
route_ids - List of Route IDs, or single Route ID
date - Time after which schedule should be returned. If included then
must be within the next seven (7) days. If not included then
schedule starting from the current datetime will be returned
max_time - Defines maximum range of time (in minutes) within which
trips will be returned. If not included defaults to 60.
max_trips - Defines number of trips to return. Integer between 1 and
100. If not included defaults to 5.
"""
params = {
"trip": trip_id,
"api_key": self.api_key,
"format": "json",
"datetime": datetime_to_epoch(date) if date else None,
}
data = self._make_request("schedulebytrip", params)
return TripSchedule(data)
def get_predictions_by_stop(self, stop_id, include_access_alerts=False,
include_service_alerts=True):
"""Return predicted arrivals and departures in the next hour for a
direction and route for a particular stop.
stop_id - Stop ID
include_access_alerts - Whether or not alerts pertaining to
accessibility (elevators, escalators) should be
returned
include_service_alerts - Whether or not service alerts should be
returned
"""
params = {
"stop": stop_id,
"api_key": self.api_key,
"format": "json",
"include_access_alerts": include_access_alerts,
"include_service_alerts": include_service_alerts
}
data = self._make_request("predictionsbystop", params)
return Prediction(data)
def get_predictions_by_routes(self, route_ids, include_access_alerts=False,
include_service_alerts=True):
"""Return predictions for upcoming trips (including trips already underway)
in a direction for a particular route or routes.
route_ids - List of Route IDs, or single Route ID
include_access_alerts - Whether or not alerts pertaining to
accessibility (elevators, escalators) should be
returned
include_service_alerts - Whether or not service alerts should be
returned
"""
if not isinstance(route_ids, list):
route_ids = [route_ids]
params = {
"routes": ",".join(route_ids),
"api_key": self.api_key,
"format": "json",
"include_access_alerts": include_access_alerts,
"include_service_alerts": include_service_alerts
}
data = self._make_request("predictionsbyroutes", params)
return Prediction(data)
def get_vehicles_by_routes(self, route_ids, include_access_alerts=False,
include_service_alerts=True):
"""Return vehicle positions for upcoming trips (including trips already
underway) in a direction for a particular route or routes.
route_ids - List of Route IDs, or single Route ID
include_access_alerts - Whether or not alerts pertaining to
accessibility (elevators, escalators) should be
returned
include_service_alerts - Whether or not service alerts should be
returned
"""
if not isinstance(route_ids, list):
route_ids = [route_ids]
params = {
"routes": ",".join(route_ids),
"api_key": self.api_key,
"format": "json",
"include_access_alerts": include_access_alerts,
"include_service_alerts": include_service_alerts
}
data = self._make_request("vehiclesbyroutes", params)
return [Mode(m) for m in data]
def get_predictions_by_trip(self, trip_id):
"""Return the predicted arrivals and departures for a particular trip.
trip_id - TripID
"""
params = {
"trip": trip_id,
"api_key": self.api_key,
"format": "json"
}
data = self._make_request("predictionsbytrip", params)
return TripSchedule(data)
def get_vehicles_by_trip(self, trip_id):
"""Return the predicted vehicle positions for a particular trip.
trip_id - TripID
"""
params = {
"trip": trip_id,
"api_key": self.api_key,
"format": "json"
}
data = self._make_request("vehiclesbytrip", params)
return TripSchedule(data)
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piotrwinkler/breast_density_classifier | density_model_torch_custom.py | 4d47dd98bb0a839cea8b9aef242f5af5db84f06f | import argparse
import glob
import os
import numpy as np
import torch
from sklearn.metrics import accuracy_score
import models_torch as models
import utils
EXPERIMENT_DATA_DIR = "/tmp/mgr"
def inference(parameters, verbose=True) -> int:
# resolve device
device = torch.device(
"cuda:{}".format(parameters["gpu_number"]) if parameters["device_type"] == "gpu"
else "cpu"
)
# load input images
datum_l_cc = utils.load_images(parameters['image_path'], 'L-CC')
datum_r_cc = utils.load_images(parameters['image_path'], 'R-CC')
datum_l_mlo = utils.load_images(parameters['image_path'], 'L-MLO')
datum_r_mlo = utils.load_images(parameters['image_path'], 'R-MLO')
# construct models and prepare data
if parameters["model_type"] == 'cnn':
model = models.BaselineBreastModel(device, nodropout_probability=1.0, gaussian_noise_std=0.0).to(device)
model.load_state_dict(torch.load(parameters["model_path"]))
x = {
"L-CC": torch.Tensor(datum_l_cc).permute(0, 3, 1, 2).to(device),
"L-MLO": torch.Tensor(datum_l_mlo).permute(0, 3, 1, 2).to(device),
"R-CC": torch.Tensor(datum_r_cc).permute(0, 3, 1, 2).to(device),
"R-MLO": torch.Tensor(datum_r_mlo).permute(0, 3, 1, 2).to(device),
}
elif parameters["model_type"] == 'histogram':
model = models.BaselineHistogramModel(num_bins=parameters["bins_histogram"]).to(device)
model.load_state_dict(torch.load(parameters["model_path"]))
x = torch.Tensor(utils.histogram_features_generator([
datum_l_cc, datum_r_cc, datum_l_mlo, datum_r_mlo
], parameters)).to(device)
else:
raise RuntimeError(parameters["model_type"])
# run prediction
with torch.no_grad():
prediction_density = model(x).cpu().numpy()
if verbose:
# nicely prints out the predictions
print('Density prediction:\n'
'\tAlmost entirely fatty (0):\t\t\t' + str(prediction_density[0, 0]) + '\n'
'\tScattered areas of fibroglandular density (1):\t' + str(prediction_density[0, 1]) + '\n'
'\tHeterogeneously dense (2):\t\t\t' + str(prediction_density[0, 2]) + '\n'
'\tExtremely dense (3):\t\t\t\t' + str(prediction_density[0, 3]) + '\n')
return np.argmax(prediction_density[0])+1 # return density in scope 1 to 4
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run Inference')
parser.add_argument('model_type')
parser.add_argument('--bins-histogram', default=50)
parser.add_argument('--model-path', default=None)
parser.add_argument('--device-type', default="cpu")
# parser.add_argument('--image-path', default="images/")
args = parser.parse_args()
parameters_ = {
"model_type": args.model_type,
"bins_histogram": args.bins_histogram,
"model_path": args.model_path,
"device_type": args.device_type,
# "image_path": args.image_path,
}
if parameters_["model_path"] is None:
if args.model_type == "histogram":
parameters_["model_path"] = "saved_models/BreastDensity_BaselineHistogramModel/model.p"
if args.model_type == "cnn":
parameters_["model_path"] = "saved_models/BreastDensity_BaselineBreastModel/model.p"
predicted_values = []
real_values = []
predicted_values_two_classes = []
real_values_two_classes = []
two_classes_mapping = {1: 0, 2: 0, 3: 1, 4: 1}
for dir in glob.glob(f"{EXPERIMENT_DATA_DIR}/*/"):
parameters_["image_path"] = dir
predicted_density = inference(parameters_)
with open(os.path.join(dir, "density.txt")) as file:
real_density = int(file.read())
print(f"Predicted density: {predicted_density}")
print(f"Real density: {real_density}\n")
print(f"Predicted density (2 cls): {two_classes_mapping[predicted_density]}")
print(f"Real density (2 cls): {two_classes_mapping[real_density]}\n")
predicted_values.append(predicted_density)
real_values.append(real_density)
predicted_values_two_classes.append(two_classes_mapping[predicted_density])
real_values_two_classes.append(two_classes_mapping[real_density])
print(f"Total accuracy: {accuracy_score(real_values, predicted_values)}")
print(f"Total accuracy two classes: {accuracy_score(real_values_two_classes, predicted_values_two_classes)}")
"""
python density_model_torch_custom.py histogram
python density_model_torch_custom.py cnn
"""
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yifatdzigan/ESMValTool | esmvaltool/diag_scripts/ensclus/ens_anom.py | 83320b0e0b24ddde965599961bb80428e180a731 | """Computation of ensemble anomalies based on a desired value."""
import os
import numpy as np
from scipy import stats
# User-defined packages
from read_netcdf import read_iris, save_n_2d_fields
from sel_season_area import sel_area, sel_season
def ens_anom(filenames, dir_output, name_outputs, varname, numens, season,
area, extreme):
"""Ensemble anomalies.
Computation of the ensemble anomalies based on the desired value
from the input variable (it can be the percentile, mean, maximum, standard
deviation or trend)
OUTPUT: NetCDF files of ensemble mean of climatology, selected value and
anomaly maps.
"""
print('The name of the output files will be <variable>_{0}.txt'
.format(name_outputs))
print('Number of ensemble members: {0}'.format(numens))
outfiles = []
# Reading the netCDF file of 3Dfield, for all the ensemble members
var_ens = []
for ens in range(numens):
ifile = filenames[ens]
# print('ENSEMBLE MEMBER %s' %ens)
var, varunits, lat, lon, dates, _ = read_iris(ifile)
# Convertion from kg m-2 s-1 to mm/day
if varunits == 'kg m-2 s-1':
var = var * 86400 # there are 86400 seconds in a day
varunits = 'mm/day'
# Selecting a season (DJF,DJFM,NDJFM,JJA)
var_season, _ = sel_season(var, dates, season)
# Selecting only [latS-latN, lonW-lonE] box region
var_area, lat_area, lon_area = sel_area(lat, lon, var_season, area)
var_ens.append(var_area)
if varunits == 'kg m-2 s-1':
print('\nPrecipitation rate units were converted from kg m-2 s-1 '
'to mm/day')
print('The variable is {0} ({1})'.format(varname, varunits))
print('Original var shape: (time x lat x lon)={0}'.format(var.shape))
print('var shape after selecting season {0} and area {1}: '
'(time x lat x lon)={2}'.format(season, area, var_area.shape))
if extreme == 'mean':
# Compute the time mean over the entire period, for each ens member
varextreme_ens = [np.nanmean(var_ens[i], axis=0)
for i in range(numens)]
elif len(extreme.split("_")) == 2:
# Compute the chosen percentile over the period, for each ens member
quant = int(extreme.partition("th")[0])
varextreme_ens = [np.nanpercentile(var_ens[i], quant, axis=0)
for i in range(numens)]
elif extreme == 'maximum':
# Compute the maximum value over the period, for each ensemble member
varextreme_ens = [np.nanmax(var_ens[i], axis=0) for i in range(numens)]
elif extreme == 'std':
# Compute the standard deviation over the period, for each ens member
varextreme_ens = [np.nanstd(var_ens[i], axis=0) for i in range(numens)]
elif extreme == 'trend':
# Compute the linear trend over the period, for each ensemble member
trendmap = np.empty((var_ens[0].shape[1], var_ens[0].shape[2]))
trendmap_ens = []
for i in range(numens):
for jla in range(var_ens[0].shape[1]):
for jlo in range(var_ens[0].shape[2]):
slope, _, _, _, _ = \
stats.linregress(range(var_ens[0].shape[0]),
var_ens[i][:, jla, jlo])
trendmap[jla, jlo] = slope
trendmap_ens.append(trendmap.copy())
varextreme_ens = trendmap_ens
varextreme_ens_np = np.array(varextreme_ens)
print('Anomalies are computed with respect to the {0}'.format(extreme))
# Compute and save the anomalies with respect to the ensemble
ens_anomalies = varextreme_ens_np - np.nanmean(varextreme_ens_np, axis=0)
varsave = 'ens_anomalies'
ofile = os.path.join(dir_output, 'ens_anomalies_{0}.nc'
.format(name_outputs))
# print(ofile)
print('ens_anomalies shape: (numens x lat x lon)={0}'
.format(ens_anomalies.shape))
save_n_2d_fields(lat_area, lon_area, ens_anomalies, varsave,
varunits, ofile)
outfiles.append(ofile)
# Compute and save the climatology
vartimemean_ens = [np.mean(var_ens[i], axis=0) for i in range(numens)]
ens_climatologies = np.array(vartimemean_ens)
varsave = 'ens_climatologies'
ofile = os.path.join(dir_output, 'ens_climatologies_{0}.nc'
.format(name_outputs))
save_n_2d_fields(lat_area, lon_area, ens_climatologies, varsave,
varunits, ofile)
outfiles.append(ofile)
ens_extreme = varextreme_ens_np
varsave = 'ens_extreme'
ofile = os.path.join(dir_output, 'ens_extreme_{0}.nc'.format(name_outputs))
save_n_2d_fields(lat_area, lon_area, ens_extreme, varsave,
varunits, ofile)
outfiles.append(ofile)
return outfiles
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Te-k/Pytition | pytition/petition/models.py | 16ebce01b491b72ed387709d9b705f7cb0d5476f | from django.db import models
from django.utils.html import mark_safe, strip_tags
from django.utils.text import slugify
from django.utils.translation import ugettext as _
from django.utils.translation import ugettext_lazy
from django.core.exceptions import ValidationError
from django.db.models.signals import post_save, post_delete
from django.dispatch import receiver
from django.conf import settings
from django.contrib.auth.hashers import get_hasher
from django.db import transaction
from django.urls import reverse
from django.db.models import Q
from tinymce import models as tinymce_models
from colorfield.fields import ColorField
import html
class Petition(models.Model):
NO = "no gradient"
RIGHT = "to right"
BOTTOM = "to bottom"
BOTTOM_RIGHT = "to bottom right"
BOTTOM_LEFT = "to bottom left"
LINEAR_GRADIENT_CHOICES = (
(NO, "no gradient"),
(RIGHT, "to right"),
(BOTTOM, "to bottom"),
(BOTTOM_RIGHT, "to bottom right"),
(BOTTOM_LEFT, "to bottom left")
)
MAIL = "MAIL"
POST = "POST"
GET = "GET"
NEWSLETTER_SUBSCRIBE_METHOD_CHOICES = (
(MAIL, "MAIL"),
(POST, "POST"),
(GET, "GET")
)
title = models.TextField(verbose_name=ugettext_lazy("Title"))
text = tinymce_models.HTMLField(blank=True)
side_text = tinymce_models.HTMLField(blank=True)
target = models.IntegerField(default=500)
linear_gradient_direction = models.CharField(choices=LINEAR_GRADIENT_CHOICES, max_length=15, default=NO, blank=True)
gradient_from = ColorField(blank=True)
gradient_to = ColorField(blank=True)
bgcolor = ColorField(blank=True)
footer_text = tinymce_models.HTMLField(blank=True)
footer_links = tinymce_models.HTMLField(blank=True)
twitter_description = models.CharField(max_length=200, blank=True)
twitter_image = models.CharField(max_length=500, blank=True)
has_newsletter = models.BooleanField(default=False)
newsletter_subscribe_http_data = models.TextField(blank=True)
newsletter_subscribe_http_mailfield = models.CharField(max_length=100, blank=True)
newsletter_subscribe_http_url = models.CharField(max_length=1000, blank=True)
newsletter_subscribe_mail_subject = models.CharField(max_length=1000, blank=True)
newsletter_subscribe_mail_from = models.CharField(max_length=500, blank=True)
newsletter_subscribe_mail_to = models.CharField(max_length=500, blank=True)
newsletter_subscribe_method = models.CharField(choices=NEWSLETTER_SUBSCRIBE_METHOD_CHOICES, max_length=4,
default=MAIL)
newsletter_subscribe_mail_smtp_host = models.CharField(max_length=100, default='localhost', blank=True)
newsletter_subscribe_mail_smtp_port = models.IntegerField(default=25, blank=True)
newsletter_subscribe_mail_smtp_user = models.CharField(max_length=200, blank=True)
newsletter_subscribe_mail_smtp_password = models.CharField(max_length=200, blank=True)
newsletter_subscribe_mail_smtp_tls = models.BooleanField(default=False)
newsletter_subscribe_mail_smtp_starttls = models.BooleanField(default=False)
org_twitter_handle = models.CharField(max_length=20, blank=True)
published = models.BooleanField(default=False)
newsletter_text = models.CharField(max_length=1000, blank=True)
sign_form_footer = models.TextField(blank=True)
confirmation_email_sender = models.CharField(max_length=100, blank=True)
confirmation_email_smtp_host = models.CharField(max_length=100, default='localhost', blank=True)
confirmation_email_smtp_port = models.IntegerField(default=25, blank=True)
confirmation_email_smtp_user = models.CharField(max_length=200, blank=True)
confirmation_email_smtp_password = models.CharField(max_length=200, blank=True)
confirmation_email_smtp_tls = models.BooleanField(default=False)
confirmation_email_smtp_starttls = models.BooleanField(default=False)
use_custom_email_settings = models.BooleanField(default=False)
salt = models.TextField(blank=True)
slugs = models.ManyToManyField('SlugModel', blank=True, through='SlugOwnership')
def prepopulate_from_template(self, template):
for field in self._meta.fields:
if hasattr(self, field.name) and hasattr(template, field.name):
template_value = getattr(template, field.name)
if template_value is not None and template_value != "":
setattr(self, field.name, template_value)
def save(self, *args, **kwargs):
super().save(*args, **kwargs)
if not self.salt:
hasher = get_hasher()
self.salt = hasher.salt().decode('utf-8')
super().save()
def slugify(self):
if self.slugs.count() == 0:
slugtext = slugify(self.raw_title)
# let's search for slug collisions
filters = {'slugs__slug': slugtext}
if self.organization_set.count() > 0:
org = self.organization_set.first()
filters.update({'organization__name': org.name})
else:
user = self.pytitionuser_set.first()
filters.update({'pytitionuser__user__username': user.user.username})
results = Petition.objects.filter(**filters)
if results.count() > 0:
raise ValueError(_("This slug is already used by another petition from this organization/user"))
slug = SlugModel(slug=slugify(slugtext))
slug.save()
self.slugs.add(slug)
self.save()
@classmethod
def by_id(cls, id):
try:
return Petition.objects.get(pk=id)
except Petition.DoesNotExist:
return None
def get_signature_number(self, confirmed=None):
signatures = self.signature_set
if confirmed is not None:
signatures = signatures.filter(confirmed=confirmed)
return signatures.count()
def already_signed(self, email):
signature_number = Signature.objects.filter(petition = self.id)\
.filter(confirmed = True).filter(email = email).count()
return signature_number > 0
def confirm_signature(self, conf_hash):
signature = Signature.objects.filter(petition=self.id).get(confirmation_hash=conf_hash)
if signature:
# Now confirm the signature corresponding to this hash
signature.confirm()
signature.save()
return _("Thank you for confirming your signature!")
else:
return None
def add_slug(self, slugtext):
with transaction.atomic():
slugtext = slugify(slugtext)
slug = SlugModel.objects.create(slug=slugtext)
if self.owner_type == "org":
SlugOwnership.objects.create(slug=slug, petition=self, organization=self.owner)
elif self.owner_type == "user":
SlugOwnership.objects.create(slug=slug, petition=self, user=self.owner)
else:
raise ValueError(_("This petition has no owner, cannot add slug!"))
def del_slug(self, slug):
slug.delete()
def publish(self):
self.published = True
self.save()
def unpublish(self):
self.published = False
self.save()
@property
def owner_type(self):
if self.organization_set.count() > 0:
return "org"
elif self.pytitionuser_set.count() > 0:
return "user"
else:
return "no_owner"
@property
def owner(self):
if self.organization_set.count() > 0:
return self.organization_set.first()
elif self.pytitionuser_set.count() > 0:
return self.pytitionuser_set.first()
else:
return None
@property
def signature_number(self):
return self.get_signature_number(True)
@property
def raw_twitter_description(self):
return html.unescape(mark_safe(strip_tags(self.twitter_description)))
@property
def raw_text(self):
return html.unescape(mark_safe(strip_tags(self.text)))
@property
def raw_title(self):
return html.unescape(mark_safe(strip_tags(self.title).strip()))
def __str__(self):
return self.raw_title
def __repr__(self):
return self.raw_title
@property
def url(self):
slugs = self.slugs.all()
if len(slugs) == 0:
# If there is no slug, ugly url
return reverse('detail', kwargs={'petition_id': self.id})
else:
if self.organization_set.count() > 0:
# This petition is owned by an Organization
org = self.organization_set.first()
return reverse("slug_show_petition",
kwargs={"orgslugname": org.slugname,
"petitionname": slugs[0]})
elif self.pytitionuser_set.count() > 0:
# This petition is owned by a PytitionUser
user = self.pytitionuser_set.first()
return reverse("slug_show_petition",
kwargs={"username": user.user.username,
"petitionname": slugs[0]})
else:
# This is a BUG!
raise ValueError(_("This petition is buggy. Sorry about that!"))
class SlugOwnership(models.Model):
petition = models.ForeignKey(Petition, on_delete=models.CASCADE)
slug = models.ForeignKey('SlugModel', on_delete=models.CASCADE)
user = models.ForeignKey('PytitionUser', on_delete=models.CASCADE, blank=True, null=True, default=None)
organization = models.ForeignKey('Organization', on_delete=models.CASCADE, blank=True, null=True, default=None)
class Meta:
constraints = [
models.UniqueConstraint(fields=['slug', 'organization'], name="unique_slugnameperorg", condition=Q(user=None)),
models.UniqueConstraint(fields=['slug', 'user'], name="unique_slugnameperuser",
condition=Q(organization=None)),
]
class Signature(models.Model):
first_name = models.CharField(max_length=50, verbose_name=ugettext_lazy("First name"))
last_name = models.CharField(max_length=50, verbose_name=ugettext_lazy("Last name"))
phone = models.CharField(max_length=20, blank=True, verbose_name=ugettext_lazy("Phone number"))
email = models.EmailField(verbose_name=ugettext_lazy("Email address"))
confirmation_hash = models.CharField(max_length=128)
confirmed = models.BooleanField(default=False, verbose_name=ugettext_lazy("Confirmed"))
petition = models.ForeignKey(Petition, on_delete=models.CASCADE, verbose_name=ugettext_lazy("Petition"))
subscribed_to_mailinglist = models.BooleanField(default=False, verbose_name=ugettext_lazy("Subscribed to mailing list"))
date = models.DateTimeField(blank=True, auto_now_add=True, verbose_name=ugettext_lazy("Date"))
ipaddress = models.TextField(blank=True, null=True)
def clean(self):
if self.petition.already_signed(self.email):
if self.petition.signature_set.filter(email = self.email).get(confirmed = True).id != self.id:
raise ValidationError(_("You already signed the petition"))
def save(self, *args, **kwargs):
self.clean()
if self.confirmed:
# invalidating other signatures from same email
Signature.objects.filter(petition=self.petition).filter(email=self.email)\
.exclude(id=self.id).delete()
super().save(*args, **kwargs)
def confirm(self):
self.confirmed = True
def __str__(self):
return html.unescape("[{}:{}] {} {}".format(self.petition.id, "OK" if self.confirmed else "..", self.first_name,
self.last_name))
def __repr__(self):
return html.unescape("[{}:{}] {} {}".format(self.petition.id, "OK" if self.confirmed else "..", self.first_name,
self.last_name))
class PetitionTemplate(models.Model):
NO = "no gradient"
RIGHT = "to right"
BOTTOM = "to bottom"
BOTTOM_RIGHT = "to bottom right"
BOTTOM_LEFT = "to bottom left"
LINEAR_GRADIENT_CHOICES = (
(NO, "no gradient"),
(RIGHT, "to right"),
(BOTTOM, "to bottom"),
(BOTTOM_RIGHT, "to bottom right"),
(BOTTOM_LEFT, "to bottom left")
)
MAIL = "MAIL"
POST = "POST"
GET = "GET"
NEWSLETTER_SUBSCRIBE_METHOD_CHOICES = (
(MAIL, "MAIL"),
(POST, "POST"),
(GET, "GET")
)
name = models.CharField(max_length=50, verbose_name=ugettext_lazy("Name"), db_index=True)
text = tinymce_models.HTMLField(blank=True)
side_text = tinymce_models.HTMLField(blank=True)
target = models.IntegerField(blank=True, null=True)
linear_gradient_direction = models.CharField(choices=LINEAR_GRADIENT_CHOICES, max_length=15, default=NO, blank=True)
gradient_from = ColorField(blank=True)
gradient_to = ColorField(blank=True)
bgcolor = ColorField(blank=True)
footer_text = tinymce_models.HTMLField(blank=True)
footer_links = tinymce_models.HTMLField(blank=True)
twitter_description = models.CharField(max_length=200, blank=True)
twitter_image = models.CharField(max_length=500, blank=True)
has_newsletter = models.BooleanField(default=False)
newsletter_subscribe_http_data = models.TextField(blank=True)
newsletter_subscribe_http_mailfield = models.CharField(max_length=100, blank=True)
newsletter_subscribe_http_url = models.CharField(max_length=1000, blank=True)
newsletter_subscribe_mail_subject = models.CharField(max_length=1000, blank=True)
newsletter_subscribe_mail_from = models.EmailField(max_length=500, blank=True)
newsletter_subscribe_mail_to = models.EmailField(max_length=500, blank=True)
newsletter_subscribe_method = models.CharField(choices=NEWSLETTER_SUBSCRIBE_METHOD_CHOICES, max_length=4,
default=MAIL)
newsletter_subscribe_mail_smtp_host = models.CharField(max_length=100, default='localhost', blank=True)
newsletter_subscribe_mail_smtp_port = models.IntegerField(default=25)
newsletter_subscribe_mail_smtp_user = models.CharField(max_length=200, blank=True)
newsletter_subscribe_mail_smtp_password = models.CharField(max_length=200, blank=True)
newsletter_subscribe_mail_smtp_tls = models.BooleanField(default=False)
newsletter_subscribe_mail_smtp_starttls = models.BooleanField(default=False)
org_twitter_handle = models.CharField(max_length=20, blank=True)
newsletter_text = models.CharField(max_length=1000, blank=True)
sign_form_footer = models.TextField(blank=True)
confirmation_email_sender = models.EmailField(max_length=100, blank=True)
confirmation_email_smtp_host = models.CharField(max_length=100, default='localhost', blank=True)
confirmation_email_smtp_port = models.IntegerField(default=25, blank=True)
confirmation_email_smtp_user = models.CharField(max_length=200, blank=True)
confirmation_email_smtp_password = models.CharField(max_length=200, blank=True)
confirmation_email_smtp_tls = models.BooleanField(default=False)
confirmation_email_smtp_starttls = models.BooleanField(default=False)
use_custom_email_settings = models.BooleanField(default=False)
def __str__(self):
return self.name
def __repr__(self):
return self.name
class Meta:
index_together = ["id", ]
class SlugModel(models.Model):
slug = models.SlugField(max_length=200)
class Meta:
constraints = [
models.UniqueConstraint(fields=['slug'], name='unique_slugname')
]
def __str__(self):
return self.slug
def __repr__(self):
return self.slug
class Organization(models.Model):
name = models.CharField(max_length=200, verbose_name=ugettext_lazy("Name"), unique=True)
petition_templates = models.ManyToManyField(PetitionTemplate, through='TemplateOwnership',
through_fields=['organization', 'template'], blank=True,
verbose_name=ugettext_lazy("Petition templates"))
petitions = models.ManyToManyField(Petition, blank=True, verbose_name=ugettext_lazy("Petitions"))
default_template = models.ForeignKey(PetitionTemplate, blank=True, null=True, related_name='+',
verbose_name=ugettext_lazy("Default petition template"), to_field='id',
on_delete=models.SET_NULL)
slugname = models.SlugField(max_length=200, unique=True)
def drop(self):
with transaction.atomic():
petitions = list(self.petitions.all())
templates = list(self.petition_templates.all())
self.delete()
for petition in petitions:
petition.delete()
for template in templates:
template.delete()
def add_member(self, member):
member.organizations.add(self)
permission = Permission.objects.create(organization=self)
permission.save()
member.permissions.add(permission)
member.save()
def __str__(self):
return self.name
def __repr__(self):
return self.name
def save(self, *args, **kwargs):
if not self.slugname:
self.slugname = slugify(self.name)
super(Organization, self).save(*args, **kwargs)
@property
def kind(self):
return "org"
@property
def fullname(self):
return self.name
def save(self, *args, **kwargs):
self.slugname = slugify(self.name)
super(Organization, self).save(*args, **kwargs)
class Permission(models.Model):
organization = models.ForeignKey(Organization, on_delete=models.CASCADE,
verbose_name=ugettext_lazy("Organization related to these permissions"))
can_add_members = models.BooleanField(default=False)
can_remove_members = models.BooleanField(default=False)
can_create_petitions = models.BooleanField(default=False)
can_modify_petitions = models.BooleanField(default=False)
can_delete_petitions = models.BooleanField(default=False)
can_create_templates = models.BooleanField(default=False)
can_modify_templates = models.BooleanField(default=False)
can_delete_templates = models.BooleanField(default=False)
can_view_signatures = models.BooleanField(default=False)
can_modify_signatures = models.BooleanField(default=False)
can_delete_signatures = models.BooleanField(default=False)
can_modify_permissions = models.BooleanField(default=False)
def set_all(self, value):
self.can_add_members = value
self.can_remove_members = value
self.can_create_petitions = value
self.can_modify_petitions = value
self.can_delete_petitions = value
self.can_create_templates = value
self.can_modify_templates = value
self.can_delete_templates = value
self.can_view_signatures = value
self.can_modify_signatures = value
self.can_delete_signatures = value
self.can_modify_permissions = value
self.save()
def __str__(self):
ret = "{orgname} : ".format(orgname=self.organization.name)
if self.user.count() > 0:
ret = ret + "{username}".format(username=self.user.all()[0].name)
else:
ret = ret + "None"
return ret
def __repr__(self):
return self.__str__()
class PytitionUser(models.Model):
petitions = models.ManyToManyField(Petition, blank=True)
organizations = models.ManyToManyField(Organization, related_name="members", blank=True)
user = models.OneToOneField(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, related_name="pytitionuser")
permissions = models.ManyToManyField(Permission, related_name="user", blank=True)
invitations = models.ManyToManyField(Organization, related_name="invited", blank=True)
petition_templates = models.ManyToManyField(PetitionTemplate, blank=True, through='TemplateOwnership',
through_fields=['user', 'template'],
verbose_name=ugettext_lazy("Petition templates"))
default_template = models.ForeignKey(PetitionTemplate, blank=True, null=True, related_name='+',
verbose_name=ugettext_lazy("Default petition template"), to_field='id',
on_delete=models.SET_NULL)
def has_right(self, right, petition=None, org=None):
if petition:
if petition in self.petitions.all():
return True
try:
if not org:
org = Organization.objects.get(petitions=petition, members=self)
permissions = self.permissions.get(organization=org)
return getattr(permissions, right)
except:
return False
if org:
try:
permissions = self.permissions.get(organization=org)
return getattr(permissions, right)
except:
return False
return False
def drop(self):
with transaction.atomic():
orgs = list(self.organizations.all())
petitions = list(self.petitions.all())
templates = list(self.petition_templates.all())
self.delete()
for org in orgs:
if org.members.count() == 0:
org.drop()
for petition in petitions:
petition.delete()
for template in templates:
template.delete()
@property
def is_authenticated(self):
return self.user.is_authenticated
@property
def name(self):
return self.username
@property
def username(self):
return self.user.username
@property
def get_full_name(self):
return self.user.get_full_name()
@property
def fullname(self):
return self.get_full_name
@property
def kind(self):
return "user"
def __str__(self):
return self.get_full_name
def __repr__(self):
return self.get_full_name
@receiver(post_save, sender=settings.AUTH_USER_MODEL)
def create_user_profile(sender, instance, created, **kwargs):
if created:
PytitionUser.objects.create(user=instance)
@receiver(post_save, sender=settings.AUTH_USER_MODEL)
def save_user_profile(sender, instance, **kwargs):
instance.pytitionuser.save()
@receiver(post_save, sender=Organization)
def save_user_profile(sender, instance, **kwargs):
if not instance.slugname:
slugtext = slugify(instance.name)
instance.slugname = slugtext
instance.save()
@receiver(post_delete, sender=PytitionUser)
def post_delete_user(sender, instance, *args, **kwargs):
if instance.user: # just in case user is not specified
instance.user.delete()
class TemplateOwnership(models.Model):
user = models.ForeignKey(PytitionUser, blank=True, null=True, on_delete=models.CASCADE)
organization = models.ForeignKey(Organization, blank=True, null=True, on_delete=models.CASCADE)
template = models.ForeignKey(PetitionTemplate, to_field='id', on_delete=models.CASCADE)
def clean(self):
if self.user is None and self.organization is None:
raise ValidationError(_("The template needs to be owned by a User or an Organization."
"It cannot hang around alone by itself."))
#class Meta:
# unique_together = (("user", "template"), ("organization", "template"))
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'from django.db import models\n'), ((382, 57, 382, 78), 'django.utils.translation.ugettext_lazy', 'ugettext_lazy', ({(382, 71, 382, 77): '"""Name"""'}, {}), "('Name')", False, 'from django.utils.translation import ugettext_lazy\n'), ((385, 61, 385, 96), 'django.utils.translation.ugettext_lazy', 'ugettext_lazy', ({(385, 75, 385, 95): '"""Petition templates"""'}, {}), "('Petition templates')", False, 'from django.utils.translation import ugettext_lazy\n'), ((386, 74, 386, 100), 'django.utils.translation.ugettext_lazy', 'ugettext_lazy', ({(386, 88, 386, 99): '"""Petitions"""'}, {}), "('Petitions')", False, 'from django.utils.translation import ugettext_lazy\n'), ((388, 54, 388, 96), 'django.utils.translation.ugettext_lazy', 'ugettext_lazy', ({(388, 68, 388, 95): '"""Default petition template"""'}, {}), "('Default petition template')", False, 'from django.utils.translation import ugettext_lazy\n'), ((393, 13, 393, 33), 'django.db.transaction.atomic', 'transaction.atomic', ({}, {}), '()', 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django.utils.translation import ugettext as _\n'), ((163, 33, 163, 82), 'django.utils.translation.ugettext', '_', ({(163, 35, 163, 81): '"""This petition has no owner, cannot add slug!"""'}, {}), "('This petition has no owner, cannot add slug!')", True, 'from django.utils.translation import ugettext as _\n'), ((208, 39, 208, 61), 'django.utils.html.strip_tags', 'strip_tags', ({(208, 50, 208, 60): 'self.title'}, {}), '(self.title)', False, 'from django.utils.html import mark_safe, strip_tags\n'), ((237, 33, 237, 79), 'django.utils.translation.ugettext', '_', ({(237, 35, 237, 78): '"""This petition is buggy. Sorry about that!"""'}, {}), "('This petition is buggy. Sorry about that!')", True, 'from django.utils.translation import ugettext as _\n')] |
JohnShullTopDev/generating-traning-data-for-healthcare-machine-learningcare- | bin/socialhistory.py | d0ffb26e1b99204a796df905b50c8caf01417f69 | import csv
from testdata import SOCIALHISTORY_FILE
from testdata import rndDate
from patient import Patient
SMOKINGCODES = {
'428041000124106': 'Current some day smoker',
'266919005' : 'Never smoker',
'449868002' : 'Current every day smoker',
'266927001' : 'Unknown if ever smoked',
'8517006' : 'Former smoker'
}
class SocialHistory(object):
"""Create instances of SocialHistory; also maintains socialHistory by patient id"""
socialHistories = {} # Dictionary of socialHistory by patient ID
@classmethod
def load(cls):
"""Loads patient SocialHistory"""
# Loop through socialHistories and build patient socialHistory lists:
histories = csv.reader(open(SOCIALHISTORY_FILE, 'U'), dialect='excel-tab')
header = next(histories)
for history in histories:
cls(dict(zip(header, history))) # Create a socialHistory instance
def __init__(self, p):
self.pid = p['PID']
self.id = p['ID']
self.smokingStatusCode = p['SMOKINGSTATUSCODE']
self.smokingStatusText = SMOKINGCODES[self.smokingStatusCode]
# Append socialHistory to the patient's socialHistory list:
if self.pid in self.__class__.socialHistories:
raise "Found >1 socialHistory for a patient"
else:
self.__class__.socialHistories[self.pid] = self
def toJSON(self, prefix=""):
if prefix:
prefix += "-"
patient = Patient.mpi[self.pid]
return {
"request": {
"method": "PUT",
"url": "Observation/" + prefix + "smokingstatus-" + self.id
},
"resource": {
"id": prefix + "smokingstatus-" + self.id,
"resourceType": "Observation",
"status": "final",
"identifier": [
{
"use" : "official",
"system": "http://www.bmc.nl/zorgportal/identifiers/observations",
"value" : prefix + self.id
}
],
"text": {
"status": "generated",
"div": '<div xmlns="http://www.w3.org/1999/xhtml">' +
'Tobacco smoking status: %s</div>'%self.smokingStatusText
},
"performer": [
{
"reference": "Practitioner/" + prefix + "Practitioner-" + patient.gp
}
],
"effectiveDateTime": rndDate(2016).isoformat(),
"code": {
"coding": [
{
"system" : "http://loinc.org",
"code" : "72166-2",
"display": "Tobacco smoking status"
}
],
"text": "Tobacco smoking status"
},
"subject": {
"reference": "Patient/" + prefix + self.pid
},
"category": [
{
"coding": [
{
"system" : "http://hl7.org/fhir/observation-category",
"code" : "social-history",
"display": "Social History"
}
],
"text": "Social History"
}
],
"valueCodeableConcept": {
"coding": [
{
"system" : "http://snomed.info/sct",
"code" : self.smokingStatusCode,
"display": self.smokingStatusText
}
],
"text": self.smokingStatusText
}
}
}
| [((74, 37, 74, 50), 'testdata.rndDate', 'rndDate', ({(74, 45, 74, 49): '(2016)'}, {}), '(2016)', False, 'from testdata import rndDate\n')] |
nirobio/puzzles | Python X/Dictionaries in python.py | fda8c84d8eefd93b40594636fb9b7f0fde02b014 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# dictionaries, look-up tables & key-value pairs\n",
"# d = {} OR d = dict()\n",
"# e.g. d = {\"George\": 24, \"Tom\": 32}\n",
"\n",
"d = {}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"d[\"George\"] = 24"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"d[\"Tom\"] = 32\n",
"d[\"Jenny\"] = 16"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'George': 24, 'Tom': 32, 'Jenny': 16}\n"
]
}
],
"source": [
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Jenny' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-0bdfff196d23>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mJenny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'Jenny' is not defined"
]
}
],
"source": [
"print(d[Jenny])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"32\n"
]
}
],
"source": [
"print(d[\"Tom\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"d[\"Jenny\"] = 20"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"print(d[\"Jenny\"])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# keys are strings or numbers \n",
"\n",
"d[10] = 100"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100\n"
]
}
],
"source": [
"print(d[10])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# how to iterate over key-value pairs"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key:\n",
"George\n",
"value:\n",
"24\n",
"\n",
"key:\n",
"Tom\n",
"value:\n",
"32\n",
"\n",
"key:\n",
"Jenny\n",
"value:\n",
"20\n",
"\n",
"key:\n",
"10\n",
"value:\n",
"100\n",
"\n"
]
}
],
"source": [
" for key, value in d.items():\n",
" print(\"key:\")\n",
" print(key)\n",
" print(\"value:\")\n",
" print(value)\n",
" print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| [] |
LiamBindle/spack | lib/spack/spack/test/cache_fetch.py | e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1 | # Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import os
import pytest
from llnl.util.filesystem import mkdirp, touch
import spack.config
from spack.fetch_strategy import CacheURLFetchStrategy, NoCacheError
from spack.stage import Stage
@pytest.mark.parametrize('_fetch_method', ['curl', 'urllib'])
def test_fetch_missing_cache(tmpdir, _fetch_method):
"""Ensure raise a missing cache file."""
testpath = str(tmpdir)
with spack.config.override('config:url_fetch_method', _fetch_method):
fetcher = CacheURLFetchStrategy(url='file:///not-a-real-cache-file')
with Stage(fetcher, path=testpath):
with pytest.raises(NoCacheError, match=r'No cache'):
fetcher.fetch()
@pytest.mark.parametrize('_fetch_method', ['curl', 'urllib'])
def test_fetch(tmpdir, _fetch_method):
"""Ensure a fetch after expanding is effectively a no-op."""
testpath = str(tmpdir)
cache = os.path.join(testpath, 'cache.tar.gz')
touch(cache)
url = 'file:///{0}'.format(cache)
with spack.config.override('config:url_fetch_method', _fetch_method):
fetcher = CacheURLFetchStrategy(url=url)
with Stage(fetcher, path=testpath) as stage:
source_path = stage.source_path
mkdirp(source_path)
fetcher.fetch()
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hanhanwu/Hanhan-Spark-Python | temp_range_sql.py | a04c33100742acffa2ad11d1937ea05c44688427 | __author__ = 'hanhanw'
import sys
from pyspark import SparkConf, SparkContext
from pyspark.sql.context import SQLContext
from pyspark.sql.types import StructType, StructField, StringType, DoubleType
conf = SparkConf().setAppName("temp range sql")
sc = SparkContext(conf=conf)
sqlContext = SQLContext(sc)
assert sc.version >= '1.5.1'
inputs1 = sys.argv[1]
output = sys.argv[2]
def get_range(recordings):
recordings.registerTempTable('Recordings')
dfrange = sqlContext.sql("""
SELECT r1.DateTime, r1.StationID, (r1.DataValue-r2.DataValue) AS Range FROM
(SELECT StationID, DateTime, Observation, DataValue FROM Recordings
WHERE Observation='TMAX') r1
JOIN
(SELECT StationID, DateTime, Observation, DataValue FROM Recordings
WHERE Observation='TMIN') r2
ON (r1.StationID = r2.StationID AND r1.DateTime = r2.DateTime)
""")
dfrange.registerTempTable('RangeTable')
df_maxrange = sqlContext.sql("""
SELECT DateTime, MAX(Range) AS MaxRange FROM RangeTable
GROUP BY DateTime
""")
df_maxrange.registerTempTable('MaxRange')
df_result = sqlContext.sql("""
SELECT t1.DateTime as DateTime, t1.StationID as StationID, t2.MaxRange as MaxRange FROM
RangeTable t1
JOIN MaxRange t2
ON (t1.DateTime = t2.DateTime AND t1.Range = t2.MaxRange)
""")
return df_result
def main():
temp_schema = StructType([
StructField('StationID', StringType(), False),
StructField('DateTime', StringType(), False),
StructField('Observation', StringType(), False),
StructField('DataValue', DoubleType(), False),
StructField('MFlag', StringType(), True),
StructField('QFlag', StringType(), True),
StructField('SFlag', StringType(), True),
StructField('OBSTime', StringType(), True),
])
df = sqlContext.read.format('com.databricks.spark.csv').options(header='false').load(inputs1, schema=temp_schema)
df = df.filter(df.QFlag == '')
dfrange = get_range(df)
result = dfrange.rdd.map(lambda r: str(r.DateTime)+' '+str(r.StationID)+' '+str(r.MaxRange))
outdata = result.sortBy(lambda r: r[0]).coalesce(1)
outdata.saveAsTextFile(output)
if __name__ == "__main__":
main()
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Pompino/react-components-23KB | container/pyf/graphqltypes/Event.py | 3201a417c5160e1b77f29fc1eac74ae9dc10d6ad | from typing_extensions import Required
#from sqlalchemy.sql.sqltypes import Boolean
from graphene import ObjectType, String, Field, ID, List, DateTime, Mutation, Boolean, Int
from models.EventsRelated.EventModel import EventModel
from graphqltypes.Utils import extractSession
class EventType(ObjectType):
id = ID()
name = String()
lastchange = DateTime()
externalId = String()
users = List('graphqltypes.User.UserType')
def resolve_users(parent, info):
session = extractSession(info)
dbRecord = session.query(EventModel).get(parent.id)
return dbRecord.users
groups = List('graphqltypes.Group.GroupType')
def resolve_users(parent, info):
session = extractSession(info)
dbRecord = session.query(EventModel).get(parent.id)
return dbRecord.groups
rooms = List('graphqltypes.Room.RoomType')
def resolve_rooms(parent, info):
session = extractSession(info)
dbRecord = session.query(EventModel).get(parent.id)
return dbRecord.rooms
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kokosing/hue | desktop/core/ext-py/openpyxl-2.3.0-b2/openpyxl/drawing/shape.py | 2307f5379a35aae9be871e836432e6f45138b3d9 | from __future__ import absolute_import
# Copyright (c) 2010-2015 openpyxl
from openpyxl.styles.colors import Color, BLACK, WHITE
from openpyxl.utils.units import (
pixels_to_EMU,
EMU_to_pixels,
short_color,
)
from openpyxl.compat import deprecated
from openpyxl.xml.functions import Element, SubElement, tostring
from openpyxl.xml.constants import (
DRAWING_NS,
SHEET_DRAWING_NS,
CHART_NS,
CHART_DRAWING_NS,
PKG_REL_NS
)
from openpyxl.compat.strings import safe_string
class Shape(object):
""" a drawing inside a chart
coordiantes are specified by the user in the axis units
"""
MARGIN_LEFT = 6 + 13 + 1
MARGIN_BOTTOM = 17 + 11
FONT_WIDTH = 7
FONT_HEIGHT = 8
ROUND_RECT = 'roundRect'
RECT = 'rect'
# other shapes to define :
'''
"line"
"lineInv"
"triangle"
"rtTriangle"
"diamond"
"parallelogram"
"trapezoid"
"nonIsoscelesTrapezoid"
"pentagon"
"hexagon"
"heptagon"
"octagon"
"decagon"
"dodecagon"
"star4"
"star5"
"star6"
"star7"
"star8"
"star10"
"star12"
"star16"
"star24"
"star32"
"roundRect"
"round1Rect"
"round2SameRect"
"round2DiagRect"
"snipRoundRect"
"snip1Rect"
"snip2SameRect"
"snip2DiagRect"
"plaque"
"ellipse"
"teardrop"
"homePlate"
"chevron"
"pieWedge"
"pie"
"blockArc"
"donut"
"noSmoking"
"rightArrow"
"leftArrow"
"upArrow"
"downArrow"
"stripedRightArrow"
"notchedRightArrow"
"bentUpArrow"
"leftRightArrow"
"upDownArrow"
"leftUpArrow"
"leftRightUpArrow"
"quadArrow"
"leftArrowCallout"
"rightArrowCallout"
"upArrowCallout"
"downArrowCallout"
"leftRightArrowCallout"
"upDownArrowCallout"
"quadArrowCallout"
"bentArrow"
"uturnArrow"
"circularArrow"
"leftCircularArrow"
"leftRightCircularArrow"
"curvedRightArrow"
"curvedLeftArrow"
"curvedUpArrow"
"curvedDownArrow"
"swooshArrow"
"cube"
"can"
"lightningBolt"
"heart"
"sun"
"moon"
"smileyFace"
"irregularSeal1"
"irregularSeal2"
"foldedCorner"
"bevel"
"frame"
"halfFrame"
"corner"
"diagStripe"
"chord"
"arc"
"leftBracket"
"rightBracket"
"leftBrace"
"rightBrace"
"bracketPair"
"bracePair"
"straightConnector1"
"bentConnector2"
"bentConnector3"
"bentConnector4"
"bentConnector5"
"curvedConnector2"
"curvedConnector3"
"curvedConnector4"
"curvedConnector5"
"callout1"
"callout2"
"callout3"
"accentCallout1"
"accentCallout2"
"accentCallout3"
"borderCallout1"
"borderCallout2"
"borderCallout3"
"accentBorderCallout1"
"accentBorderCallout2"
"accentBorderCallout3"
"wedgeRectCallout"
"wedgeRoundRectCallout"
"wedgeEllipseCallout"
"cloudCallout"
"cloud"
"ribbon"
"ribbon2"
"ellipseRibbon"
"ellipseRibbon2"
"leftRightRibbon"
"verticalScroll"
"horizontalScroll"
"wave"
"doubleWave"
"plus"
"flowChartProcess"
"flowChartDecision"
"flowChartInputOutput"
"flowChartPredefinedProcess"
"flowChartInternalStorage"
"flowChartDocument"
"flowChartMultidocument"
"flowChartTerminator"
"flowChartPreparation"
"flowChartManualInput"
"flowChartManualOperation"
"flowChartConnector"
"flowChartPunchedCard"
"flowChartPunchedTape"
"flowChartSummingJunction"
"flowChartOr"
"flowChartCollate"
"flowChartSort"
"flowChartExtract"
"flowChartMerge"
"flowChartOfflineStorage"
"flowChartOnlineStorage"
"flowChartMagneticTape"
"flowChartMagneticDisk"
"flowChartMagneticDrum"
"flowChartDisplay"
"flowChartDelay"
"flowChartAlternateProcess"
"flowChartOffpageConnector"
"actionButtonBlank"
"actionButtonHome"
"actionButtonHelp"
"actionButtonInformation"
"actionButtonForwardNext"
"actionButtonBackPrevious"
"actionButtonEnd"
"actionButtonBeginning"
"actionButtonReturn"
"actionButtonDocument"
"actionButtonSound"
"actionButtonMovie"
"gear6"
"gear9"
"funnel"
"mathPlus"
"mathMinus"
"mathMultiply"
"mathDivide"
"mathEqual"
"mathNotEqual"
"cornerTabs"
"squareTabs"
"plaqueTabs"
"chartX"
"chartStar"
"chartPlus"
'''
@deprecated("Chart Drawings need a complete rewrite")
def __init__(self,
chart,
coordinates=((0, 0), (1, 1)),
text=None,
scheme="accent1"):
self.chart = chart
self.coordinates = coordinates # in axis units
self.text = text
self.scheme = scheme
self.style = Shape.RECT
self.border_width = 0
self.border_color = BLACK # "F3B3C5"
self.color = WHITE
self.text_color = BLACK
@property
def border_color(self):
return self._border_color
@border_color.setter
def border_color(self, color):
self._border_color = short_color(color)
@property
def color(self):
return self._color
@color.setter
def color(self, color):
self._color = short_color(color)
@property
def text_color(self):
return self._text_color
@text_color.setter
def text_color(self, color):
self._text_color = short_color(color)
@property
def border_width(self):
return self._border_width
@border_width.setter
def border_width(self, w):
self._border_width = w
@property
def coordinates(self):
"""Return coordindates in axis units"""
return self._coordinates
@coordinates.setter
def coordinates(self, coords):
""" set shape coordinates in percentages (left, top, right, bottom)
"""
# this needs refactoring to reflect changes in charts
self.axis_coordinates = coords
(x1, y1), (x2, y2) = coords # bottom left, top right
drawing_width = pixels_to_EMU(self.chart.drawing.width)
drawing_height = pixels_to_EMU(self.chart.drawing.height)
plot_width = drawing_width * self.chart.width
plot_height = drawing_height * self.chart.height
margin_left = self.chart._get_margin_left() * drawing_width
xunit = plot_width / self.chart.get_x_units()
margin_top = self.chart._get_margin_top() * drawing_height
yunit = self.chart.get_y_units()
x_start = (margin_left + (float(x1) * xunit)) / drawing_width
y_start = ((margin_top
+ plot_height
- (float(y1) * yunit))
/ drawing_height)
x_end = (margin_left + (float(x2) * xunit)) / drawing_width
y_end = ((margin_top
+ plot_height
- (float(y2) * yunit))
/ drawing_height)
# allow user to specify y's in whatever order
# excel expect y_end to be lower
if y_end < y_start:
y_end, y_start = y_start, y_end
self._coordinates = (
self._norm_pct(x_start), self._norm_pct(y_start),
self._norm_pct(x_end), self._norm_pct(y_end)
)
@staticmethod
def _norm_pct(pct):
""" force shapes to appear by truncating too large sizes """
if pct > 1:
return 1
elif pct < 0:
return 0
return pct
class ShapeWriter(object):
""" one file per shape """
def __init__(self, shapes):
self._shapes = shapes
def write(self, shape_id):
root = Element('{%s}userShapes' % CHART_NS)
for shape in self._shapes:
anchor = SubElement(root, '{%s}relSizeAnchor' % CHART_DRAWING_NS)
xstart, ystart, xend, yend = shape.coordinates
_from = SubElement(anchor, '{%s}from' % CHART_DRAWING_NS)
SubElement(_from, '{%s}x' % CHART_DRAWING_NS).text = str(xstart)
SubElement(_from, '{%s}y' % CHART_DRAWING_NS).text = str(ystart)
_to = SubElement(anchor, '{%s}to' % CHART_DRAWING_NS)
SubElement(_to, '{%s}x' % CHART_DRAWING_NS).text = str(xend)
SubElement(_to, '{%s}y' % CHART_DRAWING_NS).text = str(yend)
sp = SubElement(anchor, '{%s}sp' % CHART_DRAWING_NS, {'macro':'', 'textlink':''})
nvspr = SubElement(sp, '{%s}nvSpPr' % CHART_DRAWING_NS)
SubElement(nvspr, '{%s}cNvPr' % CHART_DRAWING_NS, {'id':str(shape_id), 'name':'shape %s' % shape_id})
SubElement(nvspr, '{%s}cNvSpPr' % CHART_DRAWING_NS)
sppr = SubElement(sp, '{%s}spPr' % CHART_DRAWING_NS)
frm = SubElement(sppr, '{%s}xfrm' % DRAWING_NS,)
# no transformation
SubElement(frm, '{%s}off' % DRAWING_NS, {'x':'0', 'y':'0'})
SubElement(frm, '{%s}ext' % DRAWING_NS, {'cx':'0', 'cy':'0'})
prstgeom = SubElement(sppr, '{%s}prstGeom' % DRAWING_NS, {'prst':str(shape.style)})
SubElement(prstgeom, '{%s}avLst' % DRAWING_NS)
fill = SubElement(sppr, '{%s}solidFill' % DRAWING_NS, )
SubElement(fill, '{%s}srgbClr' % DRAWING_NS, {'val':shape.color})
border = SubElement(sppr, '{%s}ln' % DRAWING_NS, {'w':str(shape._border_width)})
sf = SubElement(border, '{%s}solidFill' % DRAWING_NS)
SubElement(sf, '{%s}srgbClr' % DRAWING_NS, {'val':shape.border_color})
self._write_style(sp)
self._write_text(sp, shape)
shape_id += 1
return tostring(root)
def _write_text(self, node, shape):
""" write text in the shape """
tx_body = SubElement(node, '{%s}txBody' % CHART_DRAWING_NS)
SubElement(tx_body, '{%s}bodyPr' % DRAWING_NS, {'vertOverflow':'clip'})
SubElement(tx_body, '{%s}lstStyle' % DRAWING_NS)
p = SubElement(tx_body, '{%s}p' % DRAWING_NS)
if shape.text:
r = SubElement(p, '{%s}r' % DRAWING_NS)
rpr = SubElement(r, '{%s}rPr' % DRAWING_NS, {'lang':'en-US'})
fill = SubElement(rpr, '{%s}solidFill' % DRAWING_NS)
SubElement(fill, '{%s}srgbClr' % DRAWING_NS, {'val':shape.text_color})
SubElement(r, '{%s}t' % DRAWING_NS).text = shape.text
else:
SubElement(p, '{%s}endParaRPr' % DRAWING_NS, {'lang':'en-US'})
def _write_style(self, node):
""" write style theme """
style = SubElement(node, '{%s}style' % CHART_DRAWING_NS)
ln_ref = SubElement(style, '{%s}lnRef' % DRAWING_NS, {'idx':'2'})
scheme_clr = SubElement(ln_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'})
SubElement(scheme_clr, '{%s}shade' % DRAWING_NS, {'val':'50000'})
fill_ref = SubElement(style, '{%s}fillRef' % DRAWING_NS, {'idx':'1'})
SubElement(fill_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'})
effect_ref = SubElement(style, '{%s}effectRef' % DRAWING_NS, {'idx':'0'})
SubElement(effect_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'})
font_ref = SubElement(style, '{%s}fontRef' % DRAWING_NS, {'idx':'minor'})
SubElement(font_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'lt1'})
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elowy01/igsr_analysis | scripts/VCF/FILTER/subset_vcf.py | ffea4885227c2299f886a4f41e70b6e1f6bb43da |
from VcfQC import VcfQC
from ReseqTrackDB import File
from ReseqTrackDB import ReseqTrackDB
import argparse
import os
import logging
import datetime
#get command line arguments
parser = argparse.ArgumentParser(description='Script to subset a VCF by excluding the variants within the regions defined by a BED file')
'''
Reseqtrack DB connection parameters
'''
parser.add_argument('--hostname', type=str, required=True, help='Hostname for ReseqTrack DB' )
parser.add_argument('--username', type=str, required=True, help='User for ReseqTrack DB' )
parser.add_argument('--port', type=int, required=True, help='Port number in the ReseqTrack DB' )
parser.add_argument('--pwd', type=str, help='PWD for the ReseqTrack DB' )
parser.add_argument('--db', type=str, required=True, help='DB name in the ReseqTrack DB' )
parser.add_argument('--type', type=str, required=True, help='Type of the new VCF file' )
parser.add_argument('--vcftools_folder', type=str, required=True, help='Folder containing the VCFtools binary' )
parser.add_argument('--bgzip_folder', type=str, required=True, help='Folder containing the bgzip binary')
parser.add_argument('--filename', type=str, required=True, help='Name (without the fullpath) of the VCF file that will be analysed. It assumes that the filename format is for example lc_bams.gatk.xxxx.vcf.gz, where lc_bams is the analysis group and gatk is the method used' )
parser.add_argument('--bed', type=str, required=True, help='BED file containing the coordinates to exclude' )
parser.add_argument('--outsuffix', type=str, required=True, help='Suffix for vcf output file. i.e. no_cms or no_offtarget' )
parser.add_argument('--outdir', type=str, required=True, help='Directory used to put the output files.' )
args = parser.parse_args()
if __name__ == '__main__':
if os.path.isdir(args.outdir) == False:
raise Exception("Output dir does not exist: %s"%args.outdir)
hostname=args.hostname
username=args.username
db=args.db
port=args.port
pwd=args.pwd
reseqdb = ReseqTrackDB(host=hostname,user=username,port=port,pwd=pwd,db=db)
file=reseqdb.fetch_file_by_filename(args.filename)
#constructing the out filename
now = datetime.datetime.now().strftime('%Y%m%d')
bits= os.path.basename(file.name).split('.')
outprefix=bits[0]+"."+bits[1]+"."+args.outsuffix+"."+now
log_filename="subset_vcf_%s.log"% outprefix
logger = logging.getLogger("subset_vcf")
logger.setLevel(logging.INFO)
# create the logging file handler
fh = logging.FileHandler(log_filename)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# add handler to logger object
logger.addHandler(fh)
logger.info("Program started")
vcfQC = VcfQC(vcf=file.path,bgzip_folder=args.bgzip_folder,vcftools_folder=args.vcftools_folder)
vcffile=vcfQC.subset_vcf(bed=args.bed,outprefix=outprefix,outdir=args.outdir,create_index=True)
f=File(path=vcffile,type=args.type,host_id=1,withdrawn=0)
f.store(reseqdb,do_md5=True)
logger.info("Done!.")
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Acidburn0zzz/helloworld | controllers/restart.py | 9d88357658c55dadf9d4c6f923b63e8cb6207f75 | import os
from base import BaseHandler
class RestartHandler(BaseHandler):
def get(self):
if not self.authenticate(superuser=True):
return
os.system('touch ' + self.application.settings["restart_path"])
self.redirect(self.get_argument("next"))
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badock/nova-tidb | nova/tests/unit/conductor/tasks/test_migrate.py | 4c4591f2cd887fdc22828e12f0c297c051bbd912 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import mock
from nova.compute import rpcapi as compute_rpcapi
from nova.conductor.tasks import migrate
from nova import objects
from nova.scheduler import client as scheduler_client
from nova.scheduler import utils as scheduler_utils
from nova import test
from nova.tests.unit.conductor.test_conductor import FakeContext
from nova.tests.unit import fake_flavor
from nova.tests.unit import fake_instance
class MigrationTaskTestCase(test.NoDBTestCase):
def setUp(self):
super(MigrationTaskTestCase, self).setUp()
self.user_id = 'fake'
self.project_id = 'fake'
self.context = FakeContext(self.user_id, self.project_id)
self.flavor = fake_flavor.fake_flavor_obj(self.context)
self.flavor.extra_specs = {'extra_specs': 'fake'}
inst = fake_instance.fake_db_instance(image_ref='image_ref',
instance_type=self.flavor)
inst_object = objects.Instance(
flavor=self.flavor,
numa_topology=None,
pci_requests=None,
system_metadata={'image_hw_disk_bus': 'scsi'})
self.instance = objects.Instance._from_db_object(
self.context, inst_object, inst, [])
self.request_spec = objects.RequestSpec(image=objects.ImageMeta())
self.hosts = [dict(host='host1', nodename=None, limits={})]
self.filter_properties = {'limits': {}, 'retry': {'num_attempts': 1,
'hosts': [['host1', None]]}}
self.reservations = []
self.clean_shutdown = True
def _generate_task(self):
return migrate.MigrationTask(self.context, self.instance, self.flavor,
self.request_spec, self.reservations,
self.clean_shutdown,
compute_rpcapi.ComputeAPI(),
scheduler_client.SchedulerClient())
@mock.patch.object(objects.RequestSpec, 'from_components')
@mock.patch.object(scheduler_utils, 'setup_instance_group')
@mock.patch.object(scheduler_client.SchedulerClient, 'select_destinations')
@mock.patch.object(compute_rpcapi.ComputeAPI, 'prep_resize')
@mock.patch.object(objects.Quotas, 'from_reservations')
def test_execute(self, quotas_mock, prep_resize_mock,
sel_dest_mock, sig_mock, request_spec_from_components):
sel_dest_mock.return_value = self.hosts
task = self._generate_task()
request_spec_from_components.return_value = self.request_spec
legacy_request_spec = self.request_spec.to_legacy_request_spec_dict()
task.execute()
quotas_mock.assert_called_once_with(self.context, self.reservations,
instance=self.instance)
sig_mock.assert_called_once_with(self.context, legacy_request_spec,
self.filter_properties)
task.scheduler_client.select_destinations.assert_called_once_with(
self.context, self.request_spec)
prep_resize_mock.assert_called_once_with(
self.context, self.instance, legacy_request_spec['image'],
self.flavor, self.hosts[0]['host'], self.reservations,
request_spec=legacy_request_spec,
filter_properties=self.filter_properties,
node=self.hosts[0]['nodename'], clean_shutdown=self.clean_shutdown)
self.assertFalse(quotas_mock.return_value.rollback.called)
def test_rollback(self):
task = self._generate_task()
task.quotas = mock.MagicMock()
task.rollback()
task.quotas.rollback.assert_called_once_with()
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maxmac12/BlackHatPython | CH7_GitCmdAndCtrl/modules/environment.py | 60044c65ffc2f1216cbf92c2ec850a4e2e9ca5bf | import os
def run(**kwargs):
print("[*] In environment module.")
return str(os.environ) | [] |
rywjhzd/Cataloging-and-Visualizing-Cradles-of-Planet-Formation | diskcatalog/core/views.py | 6d59ea9d9a07630721e19c554651bae2775962ac | from django.shortcuts import render
from .models import Disk
import os
def index(request):
context = {}
disk_list = Disk.objects.all()
context['disk_list'] = disk_list
return render(request, 'index.html', context)
#def index(request):
# module_dir = os.path.dirname(__file__)
# file_path = os.path.join(module_dir, 'data.txt')
# disk_list = open(file_path , 'r')
# data = data_file.read()
# context = {'disk_list': data}
# return render(request, 'index.html', context)
| [((10, 11, 10, 49), 'django.shortcuts.render', 'render', ({(10, 18, 10, 25): 'request', (10, 27, 10, 39): '"""index.html"""', (10, 41, 10, 48): 'context'}, {}), "(request, 'index.html', context)", False, 'from django.shortcuts import render\n')] |
guswynn/materialize | misc/python/materialize/checks/insert_select.py | f433173ed71f511d91311769ec58c2d427dd6c3b | # Copyright Materialize, Inc. and contributors. All rights reserved.
#
# Use of this software is governed by the Business Source License
# included in the LICENSE file at the root of this repository.
#
# As of the Change Date specified in that file, in accordance with
# the Business Source License, use of this software will be governed
# by the Apache License, Version 2.0.
from textwrap import dedent
from typing import List
from materialize.checks.actions import Testdrive
from materialize.checks.checks import Check
class InsertSelect(Check):
def initialize(self) -> Testdrive:
return Testdrive(
dedent(
"""
> CREATE TABLE insert_select_destination (f1 STRING);
> CREATE TABLE insert_select_source_table (f1 STRING);
> INSERT INTO insert_select_source_table SELECT 'T1' || generate_series FROM generate_series(1,10000);
"""
)
)
def manipulate(self) -> List[Testdrive]:
return [
Testdrive(dedent(s))
for s in [
"""
> INSERT INTO insert_select_source_table SELECT 'T2' || generate_series FROM generate_series(1, 10000);
> INSERT INTO insert_select_destination SELECT * FROM insert_select_source_table;
""",
"""
> INSERT INTO insert_select_source_table SELECT 'T3' || generate_series FROM generate_series(1, 10000);
> INSERT INTO insert_select_destination SELECT * FROM insert_select_source_table;
""",
]
]
def validate(self) -> Testdrive:
return Testdrive(
dedent(
"""
> SELECT LEFT(f1, 2), COUNT(*), COUNT(DISTINCT f1) FROM insert_select_destination GROUP BY LEFT(f1, 2);
T1 20000 10000
T2 20000 10000
T3 10000 10000
"""
)
)
| [((19, 12, 26, 13), 'textwrap.dedent', 'dedent', ({(20, 16, 25, 15): '"""\n > CREATE TABLE insert_select_destination (f1 STRING);\n\n > CREATE TABLE insert_select_source_table (f1 STRING);\n > INSERT INTO insert_select_source_table SELECT \'T1\' || generate_series FROM generate_series(1,10000);\n """'}, {}), '(\n """\n > CREATE TABLE insert_select_destination (f1 STRING);\n\n > CREATE TABLE insert_select_source_table (f1 STRING);\n > INSERT INTO insert_select_source_table SELECT \'T1\' || generate_series FROM generate_series(1,10000);\n """\n )', False, 'from textwrap import dedent\n'), ((48, 12, 55, 13), 'textwrap.dedent', 'dedent', ({(49, 16, 54, 14): '"""\n > SELECT LEFT(f1, 2), COUNT(*), COUNT(DISTINCT f1) FROM insert_select_destination GROUP BY LEFT(f1, 2);\n T1 20000 10000\n T2 20000 10000\n T3 10000 10000\n """'}, {}), '(\n """\n > SELECT LEFT(f1, 2), COUNT(*), COUNT(DISTINCT f1) FROM insert_select_destination GROUP BY LEFT(f1, 2);\n T1 20000 10000\n T2 20000 10000\n T3 10000 10000\n """\n )', False, 'from textwrap import dedent\n'), ((31, 22, 31, 31), 'textwrap.dedent', 'dedent', ({(31, 29, 31, 30): 's'}, {}), '(s)', False, 'from textwrap import dedent\n')] |
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