Convenience classes for assembling graph models.
deepchem.models.tf_new_models.graph_models.
AlternateSequentialWeaveGraph
(batch_size, max_atoms=50, n_atom_feat=75, n_pair_feat=14)[source]¶Bases: deepchem.models.tf_new_models.graph_models.SequentialGraph
Alternate implementation of SequentialGraph for Weave models
get_graph_topology
()¶get_layer
(layer_id)¶get_num_output_features
()¶Gets the output shape of the featurization layers of the network
return_inputs
()¶return_outputs
()¶deepchem.models.tf_new_models.graph_models.
SequentialDAGGraph
(n_atom_feat=75, max_atoms=50)[source]¶Bases: deepchem.models.tf_new_models.graph_models.SequentialGraph
SequentialGraph for DAG models
get_graph_topology
()¶get_layer
(layer_id)¶get_num_output_features
()¶Gets the output shape of the featurization layers of the network
return_inputs
()¶return_outputs
()¶deepchem.models.tf_new_models.graph_models.
SequentialDTNNGraph
(n_distance=100, distance_min=-1.0, distance_max=18.0)[source]¶Bases: deepchem.models.tf_new_models.graph_models.SequentialGraph
An analog of Keras Sequential class for Coulomb Matrix data.
automatically generates and passes topology placeholders to each layer.
get_graph_topology
()¶get_layer
(layer_id)¶get_num_output_features
()¶Gets the output shape of the featurization layers of the network
return_inputs
()¶return_outputs
()¶deepchem.models.tf_new_models.graph_models.
SequentialGraph
(n_feat)[source]¶Bases: object
An analog of Keras Sequential class for Graph data.
Like the Sequential class from Keras, but automatically passes topology placeholders from GraphTopology to each graph layer (from layers) added to the network. Non graph layers don’t get the extra placeholders.
deepchem.models.tf_new_models.graph_models.
SequentialSupportGraph
(n_feat)[source]¶Bases: object
An analog of Keras Sequential model for test/support models.
deepchem.models.tf_new_models.graph_models.
SequentialWeaveGraph
(max_atoms=50, n_atom_feat=75, n_pair_feat=14)[source]¶Bases: deepchem.models.tf_new_models.graph_models.SequentialGraph
SequentialGraph for Weave models
get_graph_topology
()¶get_layer
(layer_id)¶get_num_output_features
()¶Gets the output shape of the featurization layers of the network
return_inputs
()¶return_outputs
()¶Manages Placeholders for Graph convolution networks.
deepchem.models.tf_new_models.graph_topology.
AlternateWeaveGraphTopology
(batch_size, max_atoms=50, n_atom_feat=75, n_pair_feat=14, name='Weave_topology')[source]¶Bases: deepchem.models.tf_new_models.graph_topology.GraphTopology
Manages placeholders associated with batch of graphs and their topology
batch_to_feed_dict
(batch)[source]¶Converts the current batch of WeaveMol into tensorflow feed_dict.
Assigns the atom features and pair features to the placeholders tensors
| Returns: | feed_dict – Can be merged with other feed_dicts for input into tensorflow |
|---|---|
| Return type: | dict |
get_atom_features_placeholder
()¶get_deg_adjacency_lists_placeholders
()¶get_deg_slice_placeholder
()¶get_input_placeholders
()¶All placeholders.
Contains atom_features placeholder and topology placeholders.
get_membership_placeholder
()¶get_topology_placeholders
()¶Returns topology placeholders
Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders.
deepchem.models.tf_new_models.graph_topology.
DAGGraphTopology
(n_atom_feat=75, max_atoms=50, name='topology')[source]¶Bases: deepchem.models.tf_new_models.graph_topology.GraphTopology
GraphTopology for DAG models
batch_to_feed_dict
(batch)[source]¶Converts the current batch of mol_graphs into tensorflow feed_dict.
Assigns the graph information in array of ConvMol objects to the placeholders tensors for DAG models
| Returns: | feed_dict – Can be merged with other feed_dicts for input into tensorflow |
|---|---|
| Return type: | dict |
get_atom_features_placeholder
()¶get_deg_adjacency_lists_placeholders
()¶get_deg_slice_placeholder
()¶get_input_placeholders
()¶All placeholders.
Contains atom_features placeholder and topology placeholders.
get_membership_placeholder
()¶get_topology_placeholders
()¶Returns topology placeholders
Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders.
deepchem.models.tf_new_models.graph_topology.
DTNNGraphTopology
(n_distance=100, distance_min=-1.0, distance_max=18.0, name='DTNN_topology')[source]¶Bases: deepchem.models.tf_new_models.graph_topology.GraphTopology
Manages placeholders associated with batch of graphs and their topology
batch_to_feed_dict
(batch)[source]¶Converts the current batch of Coulomb Matrix into tensorflow feed_dict.
Assigns the atom number and distance info to the placeholders tensors
| Returns: | feed_dict – Can be merged with other feed_dicts for input into tensorflow |
|---|---|
| Return type: | dict |
get_atom_features_placeholder
()¶get_deg_adjacency_lists_placeholders
()¶get_deg_slice_placeholder
()¶get_input_placeholders
()¶All placeholders.
Contains atom_features placeholder and topology placeholders.
get_membership_placeholder
()¶get_topology_placeholders
()¶Returns topology placeholders
Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders.
deepchem.models.tf_new_models.graph_topology.
GraphTopology
(n_feat, name='topology', max_deg=10, min_deg=0)[source]¶Bases: object
Manages placeholders associated with batch of graphs and their topology
batch_to_feed_dict
(batch)[source]¶Converts the current batch of mol_graphs into tensorflow feed_dict.
Assigns the graph information in array of ConvMol objects to the placeholders tensors
| Returns: | feed_dict – Can be merged with other feed_dicts for input into tensorflow |
|---|---|
| Return type: | dict |
deepchem.models.tf_new_models.graph_topology.
WeaveGraphTopology
(max_atoms=50, n_atom_feat=75, n_pair_feat=14, name='Weave_topology')[source]¶Bases: deepchem.models.tf_new_models.graph_topology.GraphTopology
Manages placeholders associated with batch of graphs and their topology
batch_to_feed_dict
(batch)[source]¶Converts the current batch of WeaveMol into tensorflow feed_dict.
Assigns the atom features and pair features to the placeholders tensors
| Returns: | feed_dict – Can be merged with other feed_dicts for input into tensorflow |
|---|---|
| Return type: | dict |
get_atom_features_placeholder
()¶get_deg_adjacency_lists_placeholders
()¶get_deg_slice_placeholder
()¶get_input_placeholders
()¶All placeholders.
Contains atom_features placeholder and topology placeholders.
get_membership_placeholder
()¶get_topology_placeholders
()¶Returns topology placeholders
Consists of deg_slice_placeholder, membership_placeholder, and the deg_adj_list_placeholders.
Implements a multitask graph convolutional classifier.
deepchem.models.tf_new_models.multitask_classifier.
MultitaskGraphClassifier
(model, n_tasks, n_feat, logdir=None, batch_size=50, final_loss='cross_entropy', learning_rate=0.001, optimizer_type='adam', learning_rate_decay_time=1000, beta1=0.9, beta2=0.999, pad_batches=True, verbose=True)[source]¶Bases: deepchem.models.models.Model
construct_feed_dict
(X_b, y_b=None, w_b=None, training=True)[source]¶Get initial information about task normalization
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit
(dataset, nb_epoch=10, max_checkpoints_to_keep=5, log_every_N_batches=50, checkpoint_interval=10, **kwargs)[source]¶fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict_proba
(dataset, transformers=[], n_classes=2, **kwargs)[source]¶Wraps predict_proba to set batch_size/padding.
reload
()¶Reload trained model from disk.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
Implements a multitask graph-convolutional regression.
deepchem.models.tf_new_models.multitask_regressor.
DTNNMultitaskGraphRegressor
(model, n_tasks, n_feat, logdir=None, batch_size=50, final_loss='weighted_L2', learning_rate=0.001, optimizer_type='adam', learning_rate_decay_time=1000, beta1=0.9, beta2=0.999, pad_batches=True, verbose=True)[source]¶Bases: deepchem.models.tf_new_models.multitask_regressor.MultitaskGraphRegressor
add_optimizer
()¶add_training_loss
(final_loss, outputs)¶Computes loss using logits.
construct_feed_dict
(X_b, y_b=None, w_b=None, training=True)¶Get initial information about task normalization
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit
(dataset, nb_epoch=10, max_checkpoints_to_keep=5, log_every_N_batches=50, checkpoint_interval=10, **kwargs)¶fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_num_tasks
()¶Needed to use Model.predict() from superclass.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict
(dataset, transformers=[], **kwargs)¶Wraps predict to set batch_size/padding.
predict_on_batch
(X)¶Return model output for the provided input.
predict_proba
(dataset, transformers=[], batch_size=None, n_classes=2)¶TODO: Do transformers even make sense here?
| Returns: | numpy ndarray of shape (n_samples, n_classes*n_tasks) |
|---|---|
| Return type: | y_pred |
predict_proba_on_batch
(X)¶Makes predictions of class probabilities on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
reload
()¶Reload trained model from disk.
save
()¶No-op since this model doesn’t currently support saving...
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
deepchem.models.tf_new_models.multitask_regressor.
MultitaskGraphRegressor
(model, n_tasks, n_feat, logdir=None, batch_size=50, final_loss='weighted_L2', learning_rate=0.001, optimizer_type='adam', learning_rate_decay_time=1000, beta1=0.9, beta2=0.999, pad_batches=True, verbose=True)[source]¶Bases: deepchem.models.models.Model
construct_feed_dict
(X_b, y_b=None, w_b=None, training=True)[source]¶Get initial information about task normalization
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit
(dataset, nb_epoch=10, max_checkpoints_to_keep=5, log_every_N_batches=50, checkpoint_interval=10, **kwargs)[source]¶fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict_proba
(dataset, transformers=[], batch_size=None, n_classes=2)¶TODO: Do transformers even make sense here?
| Returns: | numpy ndarray of shape (n_samples, n_classes*n_tasks) |
|---|---|
| Return type: | y_pred |
predict_proba_on_batch
(X)¶Makes predictions of class probabilities on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
reload
()¶Reload trained model from disk.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
Train support-based models.
deepchem.models.tf_new_models.support_classifier.
SupportGraphClassifier
(model, test_batch_size=10, support_batch_size=10, learning_rate=0.001, similarity='cosine', **kwargs)[source]¶Bases: deepchem.models.models.Model
construct_feed_dict
(test, support, training=True, add_phase=False)[source]¶Constructs tensorflow feed from test/support sets.
evaluate
(dataset, metric, n_pos, n_neg, n_trials=1000, exclude_support=True)[source]¶Evaluate performance on dataset according to metrics
Evaluates the performance of the trained model by sampling supports randomly for each task in dataset. For each sampled support, the accuracy of the model with support provided is computed on all data for that task. If exclude_support is True (by default), the support set is excluded from this accuracy calculation. exclude_support should be set to false if model’s memorization capacity wants to be evaluated.
Since the accuracy on a task is dependent on the choice of random support, the evaluation experiment is repeated n_trials times for each task. (Each task gets n_trials experiments). The computed accuracies are averaged across trials.
TODO(rbharath): Currently does not support any transformers.
| Parameters: |
|
|---|
fit
(dataset, n_episodes_per_epoch=1000, nb_epochs=1, n_pos=1, n_neg=9, log_every_n_samples=10, **kwargs)[source]¶Fits model on dataset using cached supports.
For each epcoh, sample n_episodes_per_epoch (support, test) pairs and does gradient descent.
| Parameters: |
|
|---|
fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_num_tasks
()¶Get number of tasks.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_scores
()[source]¶Adds tensor operations for computing scores.
Computes prediction yhat (eqn (1) in Matching networks) of class for test compounds.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict
(support, test)[source]¶Makes predictions on test given support.
TODO(rbharath): Does not currently support any transforms. TODO(rbharath): Only for 1 task at a time currently. Is there a better way?
predict_proba
(support, test)[source]¶Makes predictions on test given support.
TODO(rbharath): Does not currently support any transforms. TODO(rbharath): Only for 1 task at a time currently. Is there a better way? :param support: The support dataset :type support: dc.data.Dataset :param test: The test dataset
reload
()¶Reload trained model from disk.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
Implements Autodock Vina’s pose-generation in tensorflow.
deepchem.models.tf_new_models.vina_model.
VinaModel
(max_local_steps=10, max_mutations=10)[source]¶Bases: deepchem.models.models.Model
construct_graph
(N_protein=1000, N_ligand=100, M=50, ndim=3, k=5, nbr_cutoff=6)[source]¶Builds the computational graph for Vina.
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit_on_batch
(X, y, w)¶Updates existing model with new information.
generate_conformation
(protein, ligand, max_steps=10)[source]¶Performs the global search for conformations.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_num_tasks
()¶Get number of tasks.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict
(dataset, transformers=[], batch_size=None)¶Uses self to make predictions on provided Dataset object.
| Returns: | numpy ndarray of shape (n_samples,) |
|---|---|
| Return type: | y_pred |
predict_on_batch
(X, **kwargs)¶Makes predictions on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
predict_proba
(dataset, transformers=[], batch_size=None, n_classes=2)¶TODO: Do transformers even make sense here?
| Returns: | numpy ndarray of shape (n_samples, n_classes*n_tasks) |
|---|---|
| Return type: | y_pred |
predict_proba_on_batch
(X)¶Makes predictions of class probabilities on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
reload
()¶Reload trained model from disk.
save
()¶Dispatcher function for saving.
Each subclass is responsible for overriding this method.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
deepchem.models.tf_new_models.vina_model.
compute_closest_neighbors
(coords, cells, atoms_in_cells, neighbor_cells, N, n_cells, ndim=3, k=5)[source]¶Computes nearest neighbors from neighboring cells.
TODO(rbharath): Make this pass test
| Parameters: |
|---|
deepchem.models.tf_new_models.vina_model.
compute_neighbor_cells
(cells, ndim, n_cells)[source]¶Compute neighbors of cells in grid.
# TODO(rbharath): Do we need to handle periodic boundary conditions properly here? # TODO(rbharath): This doesn’t handle boundaries well. We hard-code # looking for 26 neighbors, which isn’t right for boundary cells in # the cube.
Note n_cells is box_size**ndim. 26 is the number of neighbors of a cube in a grid (including diagonals).
| Parameters: | cells (tf.Tensor) – (n_cells, 26) shape. |
|---|
deepchem.models.tf_new_models.vina_model.
compute_neighbor_list
(coords, nbr_cutoff, N, M, n_cells, ndim=3, k=5)[source]¶Computes a neighbor list from atom coordinates.
| Parameters: | |
|---|---|
| Returns: | nbr_list – Shape (N, M) of atom indices |
| Return type: | tf.Tensor |
deepchem.models.tf_new_models.vina_model.
cutoff
(d, x)[source]¶Truncates interactions that are too far away.
deepchem.models.tf_new_models.vina_model.
g
(c, Nrot)[source]¶Nonlinear function mapping interactions to free energy.
deepchem.models.tf_new_models.vina_model.
gauss_1
(d)[source]¶Computes first Gaussian interaction term.
Note that d must be in Angstrom
deepchem.models.tf_new_models.vina_model.
gauss_2
(d)[source]¶Computes second Gaussian interaction term.
Note that d must be in Angstrom.
deepchem.models.tf_new_models.vina_model.
get_cells
(start, stop, nbr_cutoff, ndim=3)[source]¶Returns the locations of all grid points in box.
Suppose start is -10 Angstrom, stop is 10 Angstrom, nbr_cutoff is 1. Then would return a list of length 20^3 whose entries would be [(-10, -10, -10), (-10, -10, -9), ..., (9, 9, 9)]
| Returns: | cells – (box_size**ndim, ndim) shape. |
|---|---|
| Return type: | tf.Tensor |
deepchem.models.tf_new_models.vina_model.
get_cells_for_atoms
(coords, cells, N, n_cells, ndim=3)[source]¶Compute the cells each atom belongs to.
| Parameters: |
|
|---|---|
| Returns: | cells_for_atoms – Shape (N, 1) |
| Return type: | tf.Tensor |
deepchem.models.tf_new_models.vina_model.
h
(d)[source]¶Sum of energy terms used in Autodock Vina.
deepchem.models.tf_new_models.vina_model.
hydrophobic
(d)[source]¶Compute hydrophobic interaction term.


{% extends "base.html" %}
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LogMyExercise is here to help you easily log your workouts and be inspired with ones recommended by our user community.
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{% extends 'blog/base.html' %} {% block body %} {{ count }} results found based on your query.
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{% include 'head.html' { page: "index" }%}
TED is a media organisation which posts talks online for free distribution under the slogan “ideas worth spreading”.
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Format
How to format numbers, currencies, etc, and unformat them.
Format
Numbers can be formatted to look like currency, percentages, times, or even plain old numbers with decimal places, thousands, and abbreviations.
Numbers
| Number | Format | String |
|---|---|---|
| 10000 | '0,0.0000' | |
| 10000.23 | '0,0' | |
| 10000.23 | '+0,0' | |
| -10000 | '0,0.0' | |
| 10000.1234 | '0.000' | |
| 10000.1234 | '0[.]00000' | |
| -10000 | '(0,0.0000)' | |
| -0.23 | '.00' | |
| -0.23 | '(.00)' | |
| 0.23 | '0.00000' | |
| 0.23 | '0.0[0000]' | |
| 1230974 | '0.0a' | |
| 1460 | '0 a' | |
| -104000 | '0a' | |
| 233434 | '0a.00' | |
| 233000 | '0a.00' | |
| 1 | '0o' | |
| 52 | '0o' | |
| 23 | '0o' | |
| 100 | '0o' |
Average
numbro provides an easy mechanism to round up any number with the special formatting character a
.
Note that the delimiters are per language.
| Number | Format | String |
|---|---|---|
| 123 | '0a' | |
| 1234 | '0a' | |
| 12345 | '0a' | |
| 123456 | '0a' | |
| 1234567 | '0a' | |
| 12345678 | '0a' | |
| 123456789 | '0a' |
In addition to this, when numbers are rounded up, one can specify the precision wanted with a format like 3a
(for 3 numbers only).
In this case, 0
is used for "automatic" mode.
| Number | Format | String |
|---|---|---|
|<PHONE_NUMBER> | '0a' | |
|<PHONE_NUMBER> | '1a' | |
|<PHONE_NUMBER> | '2a' | |
|<PHONE_NUMBER> | '3a' | |
|<PHONE_NUMBER> | '4a' | |
|<PHONE_NUMBER> | '5a' | |
|<PHONE_NUMBER> | '6a' | |
|<PHONE_NUMBER> | '7a' | |
|<PHONE_NUMBER> | '8a' | |
|<PHONE_NUMBER> | '9a' | |
|<PHONE_NUMBER> | '10a' |
Currency
numbro supports cultural currency formatting via the function formatCurrency
.
| Number | Language | String |
|---|---|---|
| 1000.234 | en-US | |
| 1000.234 | fr-FR | |
| 1000.234 | ja-JP | |
| 1000.234 | ru-RU |
You can of course precise a more specific format, as long as you do not use the $
(that would override defaults).
| Number | Language | String |
|---|---|---|
| 1000.234 | en-US | |
| 1000.234 | fr-FR | |
| 1000.234 | ja-JP | |
| 1000.234 | ru-RU |
But if you want to, you can always precise a format by hand to override the languages defaults.
| Number | Format | String |
|---|---|---|
| 1000.234 | '$0,0.00' | |
| 1000.2 | '0,0[.]00 $' | |
| 1001 | '$ 0,0[.]00' | |
| -1000.234 | '($0,0)' | |
| -1000.234 | '$0.00' | |
| 1230974 | '($ 0.00 a)' |
Note that you can set the default format for currency formatting using the function numbro.defaultCurrencyFormat
. The current culture defaults are kept (symbol, symbol position, etc).
You can even round the result with your own rounding function (default is Math.round
).
Bytes
| Number | Format | String |
|---|---|---|
| 100 | '0b' | |
| 2048 | '0 b' | |
|<PHONE_NUMBER> | '0.0b' | |
|<PHONE_NUMBER>787 | '0.000 b' |
Percentages
| Number | Format | String |
|---|---|---|
| 1 | '0%' | |
| 0.974878234 | '0.000%' | |
| -0.43 | '0 %' | |
| 0.43 | '(0.000 %)' |
Time
| Number | Format | String |
|---|---|---|
| 25 | '00:00:00' | |
| 238 | '00:00:00' | |
| 63846 | '00:00:00' |
If you want to format time more than that, we recommend you Momentjs which inspired numbro.
Unformat
Got a formatted string? Use the unformat
function to make it useful again.
| String | Function | Number |
|---|---|---|
| '10,000.123' | .unformat('10,000.123') | |
| '0.12345' | .unformat('0.12345') | |
| '1.23m' | .unformat('1.23m') | |
| '23rd' | .unformat('23rd') | |
| '$10,000.00' | .unformat('$10,000.00') | |
| '100B' | .unformat('100B') | |
| '3.154TB' | .unformat('3.154TB') | |
| '-76%' | .unformat('-76%') | |
| '2:23:57' | .unformat('2:23:57') |
Defaults
numbro allows you to the default format to use, as well as how to format 0.
Default format
Set a default format so you can use .format()
without a string. The default format to '0,0'
.
Default zero
Set a custom output when formatting numbers with a value of 0
.


Sphinx FAQ¶
This is a list of Frequently Asked Questions about Sphinx. Feel free to suggest new entries!
How do I…¶
- … create PDF files without LaTeX?
rinohtype provides a PDF builder that can be used as a drop-in replacement for the LaTeX builder.
- … get section numbers?
They are automatic in LaTeX output; for HTML, give a
:numbered:
option to thetoctree
directive where you want to start numbering.- … customize the look of the built HTML files?
Use themes, see /usage/theming.
- … add global substitutions or includes?
Add them in the :confval:`rst_prolog` or :confval:`rst_epilog` config value.
- … display the whole TOC tree in the sidebar?
Use the
toctree
callable in a custom layout template, probably in thesidebartoc
block.- … write my own extension?
See the /development/tutorials/index.
- … convert from my existing docs using MoinMoin markup?
The easiest way is to convert to xhtml, then convert xhtml to reST. You’ll still need to mark up classes and such, but the headings and code examples come through cleanly.
For many more extensions and other contributed stuff, see the sphinx-contrib repository.
Using Sphinx with…¶
- Read the Docs
Read the Docs is a documentation hosting service based around Sphinx. They will host sphinx documentation, along with supporting a number of other features including version support, PDF generation, and more. The Getting Started guide is a good place to start.
- Epydoc
There’s a third-party extension providing an api role which refers to Epydoc’s API docs for a given identifier.
- Doxygen
Michael Jones is developing a reST/Sphinx bridge to doxygen called breathe.
- SCons
Glenn Hutchings has written a SCons build script to build Sphinx documentation; it is hosted here: https://bitbucket.org/zondo/sphinx-scons
- PyPI
Jannis Leidel wrote a setuptools command that automatically uploads Sphinx documentation to the PyPI package documentation area at https://pythonhosted.org/.
- GitHub Pages
Please add
sphinx.ext.githubpages
to your project. It allows you to publish your document in GitHub Pages. It generates helper files for GitHub Pages on building HTML document automatically.- MediaWiki
See https://bitbucket.org/kevindunn/sphinx-wiki/wiki/Home, a project by Kevin Dunn.
- Google Analytics
You can use a custom
layout.html
template, like this:{% extends "!layout.html" %} {%- block extrahead %} {{ super() }}  {% endblock %} {% block footer %} {{ super() }}
This page uses  Google Analytics to collect statistics. You can disable it by blocking the JavaScript coming from www.google-analytics.com.
 {% endblock %}
- Google Search
To replace Sphinx’s built-in search function with Google Search, proceed as follows:
Go to https://cse.google.com/cse/all to create the Google Search code snippet.
Copy the code snippet and paste it into
_templates/searchbox.html
in your Sphinx project:

{{ _('Quick search') }}
  <gcse:search></gcse:search>

Add
searchbox.html
to the :confval:`html_sidebars` configuration value.
Sphinx vs. Docutils¶
tl;dr: docutils converts reStructuredText to multiple output formats. Sphinx builds upon docutils to allow construction of cross-referenced and indexed bodies of documentation.
docutils is a text processing system for converting plain text documentation into other, richer formats. As noted in the docutils documentation, docutils uses readers to read a document, parsers for parsing plain text formats into an internal tree representation made up of different types of nodes, and writers to output this tree in various document formats. docutils provides parsers for one plain text format - reStructuredText - though other, out-of-tree parsers have been implemented including Sphinx’s Markdown parser. On the other hand, it provides writers for many different formats including HTML, LaTeX, man pages, Open Document Format and XML.
docutils exposes all of its functionality through a variety of front-end
tools, such as rst2html
, rst2odt
and rst2xml
. Crucially though,
all of these tools, and docutils itself, are concerned with individual
documents. They don’t support concepts such as cross-referencing, indexing of
documents, or the construction of a document hierarchy (typically manifesting
in a table of contents).
Sphinx builds upon docutils by harnessing docutils’ readers and parsers and providing its own /usage/builders/index. As a result, Sphinx wraps some of the writers provided by docutils. This allows Sphinx to provide many features that would simply not be possible with docutils, such as those outlined above.
Epub info¶
The following list gives some hints for the creation of epub files:
Split the text into several files. The longer the individual HTML files are, the longer it takes the ebook reader to render them. In extreme cases, the rendering can take up to one minute.
Try to minimize the markup. This also pays in rendering time.
For some readers you can use embedded or external fonts using the CSS
@font-face
directive. This is extremely useful for code listings which are often cut at the right margin. The default Courier font (or variant) is quite wide and you can only display up to 60 characters on a line. If you replace it with a narrower font, you can get more characters on a line. You may even use FontForge and create narrow variants of some free font. In my case I get up to 70 characters on a line.You may have to experiment a little until you get reasonable results.
Test the created epubs. You can use several alternatives. The ones I am aware of are Epubcheck, Calibre, FBreader (although it does not render the CSS), and Bookworm. For Bookworm, you can download the source from https://code.google.com/archive/p/threepress and run your own local server.
Large floating divs are not displayed properly. If they cover more than one page, the div is only shown on the first page. In that case you can copy the
epub.css
from thesphinx/themes/epub/static/
directory to your local_static/
directory and remove the float settings.Files that are inserted outside of the
toctree
directive must be manually included. This sometimes applies to appendixes, e.g. the glossary or the indices. You can add them with the :confval:`epub_post_files` option.The handling of the epub cover page differs from the reStructuredText procedure which automatically resolves image paths and puts the images into the
_images
directory. For the epub cover page put the image in the :confval:`html_static_path` directory and reference it with its full path in the :confval:`epub_cover` config option.kindlegen command can convert from epub3 resulting file to
.mobi
file for Kindle. You can getyourdoc.mobi
under_build/epub
after the following command:$ make epub $ kindlegen _build/epub/yourdoc.epub
The kindlegen command doesn’t accept documents that have section titles surrounding
toctree
directive:Section Title ============= .. toctree:: subdocument Section After Toc Tree ======================
kindlegen assumes all documents order in line, but the resulting document has complicated order for kindlegen:
``parent.xhtml`` -> ``child.xhtml`` -> ``parent.xhtml``
If you get the following error, fix your document structure:
Error(prcgen):E24011: TOC section scope is not included in the parent chapter:(title) Error(prcgen):E24001: The table of content could not be built.
Texinfo info¶
There are two main programs for reading Info files, info
and GNU Emacs. The
info
program has less features but is available in most Unix environments
and can be quickly accessed from the terminal. Emacs provides better font and
color display and supports extensive customization (of course).
Displaying Links¶
One noticeable problem you may encounter with the generated Info files is how references are displayed. If you read the source of an Info file, a reference to this section would look like:
* note Displaying Links: target-id
In the stand-alone reader, info
, references are displayed just as they
appear in the source. Emacs, on the other-hand, will by default replace
*note:
with see
and hide the target-id
. For example:
The exact behavior of how Emacs displays references is dependent on the variable
Info-hide-note-references
. If set to the value of hide
, Emacs will hide
both the *note:
part and the target-id
. This is generally the best way
to view Sphinx-based documents since they often make frequent use of links and
do not take this limitation into account. However, changing this variable
affects how all Info documents are displayed and most do take this behavior
into account.
If you want Emacs to display Info files produced by Sphinx using the value
hide
for Info-hide-note-references
and the default value for all other
Info files, try adding the following Emacs Lisp code to your start-up file,
~/.emacs.d/init.el
.
(defadvice info-insert-file-contents (after
sphinx-info-insert-file-contents
activate)
"Hack to make `Info-hide-note-references' buffer-local and
automatically set to `hide' iff it can be determined that this file
was created from a Texinfo file generated by Docutils or Sphinx."
(set (make-local-variable 'Info-hide-note-references)
(default-value 'Info-hide-note-references))
(save-excursion
(save-restriction
(widen) (goto-char (point-min))
(when (re-search-forward
"^Generated by \\(Sphinx\\|Docutils\\)"
(save-excursion (search-forward "\x1f" nil t)) t)
(set (make-local-variable 'Info-hide-note-references)
'hide)))))
Notes¶
The following notes may be helpful if you want to create Texinfo files:
Each section corresponds to a different
node
in the Info file.Colons (
:
) cannot be properly escaped in menu entries and xrefs. They will be replaced with semicolons (;
).Links to external Info files can be created using the somewhat official URI scheme
info
. For example:info:Texinfo#makeinfo_options
Inline markup
The standard formatting for
*strong*
and_emphasis_
can result in ambiguous output when used to markup parameter names and other values. Since this is a fairly common practice, the default formatting has been changed so thatemphasis
andstrong
are now displayed like`literal'
s.The standard formatting can be re-enabled by adding the following to your
conf.py
:texinfo_elements = {'preamble': """ @definfoenclose strong,*,* @definfoenclose emph,_,_ """}


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30 Tips for Software Engineering Careers, After 30 Years of Life
I turned 30, a few days ago. An interesting decade, where I did and learned a lot of things came to an end. I tend to keep track of such “life lessons”, so I thought it would be a good idea to publicize the most related to our work life as software engineers.
Here we go:
1. Work really hard
No one will grant you a promotion, salary increase or a better position for nothing. And plain hard work is not enough; you need to be efficient as well.
Working hard and delivering no value is useful for no one, bar people who count productivity by the number of hours you sit on your chair.
And by the way you need to avoid those people, the companies they work for, are usually super-toxic and will make you miserable. Thankfully, those are becoming extinct in our field.
2. Don’t stick to programming languages
There is no point in giving too many fucks about a language. Most of them work around the same principles and just do certain things better than others (which is of course important). That’s why we have a lot, because no language is perfect.
Make sure though you know at leastone language from each paradigm. For example functional, object-oriented, etc.
3. Learn a new language of technology every year
Try to learn one new language (ideally a language that is popular and pushes you out of comfort zone) every year to keep your brain sharp and keep up with the trends of the market.
Also, if you find value on the language you learned, you can propose it as a tool to a certain problem to your company.
4. Take full responsibility for your career
You had a tough childhood, your ex-manager (or the current one) was a jerk, you had 5 divorces in 3 different marriages and the web framework you invested 2 years to master is not popular anymore.  There is no positivity around.
 Having a tough past is not an excuse for not being your best self now. Focus on what you can do to make things better and make sure that you… =>
5. Don’t worry about things that are out of your own control
Only worry about having the proper response to them. As mentioned before, the web framework you invested about 3 years to master is not popular anymore. So what? Learn another one. Maybe pick a framework that looks to have better prospects. And you will need to do this quite a few times in your career life.
Maybe your (beloved) friend-tech lead has left the company. It is very sad (happened to me in the past too), but it is also time to gear up and impress the new one.
If you cant handle it, you can leave the company as well.  Rather, adjust your response, as the show must go on.
6. Avoid hatred
If someone is better than you on something don’t hate them, learn from them. We consider people that are better than us, as threats. I choose to treat them as potential sources of improvement and you should do the same.
I remember a couple of colleagues from past employers who were extremely good to handle pressure and come up with solutions on thin-ice-problems. I have learned a lot from them, I never felt envy, because I knew I could acquire that skill as well.
Beware, if for any reason there are no better people (technically) than you around, it’s time to find a better place for you and your career. Trust me you don’t want to be the best unless you are the CTO or something similar. But even then as the common saying goes, you need to hire people better than you.
Hatred can ruin your personal life too, in terms of mood and reduce positivity in your life. You have nothing to gain by hating people.
7. Chase responsibility and money will follow
I’m sad that too many people are focused solely on money and not to build a strong skillset and brand for themselves . When do you think you will have better monetary prospects in the future: If you are an underpaid CTO or an overpaid Junior engineer. Choose your jobs wisely.
8. If you neglect to sharpen your skills, they will eventually abandon you
Too many people think that a computer science degree made them some kind of prince/princess, where the companies will beg them to join them and everything will be handed over easily, just because they have a piece of paper on their walls.
Don’t get me wrong, I believe a good degree can give you loads of useful knowledge (I have an undergraduate and soon a MSc in informatics) but too many people stop there.
This is where I admire self-taught programmers. They may lack knowledge on various theoretical things of computer science, but they know the hard way, no one will give them any piece of success for free.
Also, if you don’t know (at the bare minimum) some basic things about the most popular trends, you are doing something wrong.
9. Cheap Hardware is cheap on quality, price and ruins your health as well
Invest in an above-average keyboard, pointing device and monitor. Your career depends a lot on your hands and eyes.
Having noise cancellation headphones is great, but don’t buy them because everyone has them, if you don’t have high quality hardware of the previously mentioned components.
Unless you are working in an extremely noisy environment you can probably do without noise cancelling headphones.
10. Take a walk and the problem will be solved
Okay I am exaggerating a bit, but I want to emphasize the necessity of diffuse mode, Barbara Oakley explains in her course Learning how to learn (which I highly recommend).
Nowadays people don’t put much value in the diffuse mode of thinking. At least I know only a few companies that approve sleeping (or other diffuse frienly activities) during working hours, but the thing is that we need both. Actually the very famous expression “sleep on it” is implying the importance of diffuse mode.
11. Invest a fair amount of your income professional education
If your company can pay that money, thats even better.
YouTube is a fantastic resource of learning, but if you are really serious for your craft and of course your income allows you so, you should have a subscription to a high-quality training service like O’Reilly / Pluralsight.
Find the learning method you like better and learn more by using it. Speaking of professional education =>
12. Avoid companies which have no training policy
I might be very hard and biased here. You may allow exceptions where the company is fairly new to the market or have low funding.
Given that the field requires constantly updating your knowledge and being up-to-date, I tend to not take companies that don’t have a proper training/education policy, seriously into consideration.
The bare minimum of proper education policy for me is, each employee to have a dedicated budget, which can at least cover (annually)
- Attending a conference
- Buying a couple of books
- Buying a subscription to a high-quality training service like O’Reilly learning
- Do a certification
Of course, most people will not do all those in a year, for many reasons; maybe because they have settled or because they have a family and they cannot dedicate much of their free time, but the thing is that the company should not be an obstacle if the employee wants to do so.
I am more demanding for big companies, where I would expect (additionally) some invited lectures on-premises, especially on domain critical things. For example, if the company is migrating to Scrum it is good to bring an Agile coach around to set the stage.
13. Use the best tools money can buy
For example your IDE. In the same logic as in hardware; don’t go cheap on your IDE. As it is said in the pragmatic programmer book, pick an editor and learn it very very well. High quality tooling can literally save you hours.
Dont forget that money are pretty much compressed time. You pay a higher monetary price now and you save time later.
14. Ignore motivation
People tend to rely heavily on motivation towards their important life goals. Motivation is great but it is still a sentiment. And as all sentiments, they come and go.
You ’d better have a more optimal way to guide yourself (like your dream life or a vision as discussed in the next point), on what needs to be done, at least on the important things of life.
It is easy to put on your smiley face when everything is going well, but what do you do, when nothing can motivate you and you are approaching a burndown state, when nothing is fascinating anymore?
15. Keep the fire in you, alive, at all costs
While I do not recommend hanging on your motivation to progress your career and your life in general, I would insist on having a vision for your career.
You should totally have a vision of where you want to go so that you know if every decision you make (not only career-wise) will make your ship move faster and safer. As the famous British rowmen medalists in 2000 used to say, will it make the boat go faster?
16. Understanding which type of company fits you better and focus there on getting similar jobs
Startups, mid-sized companies, and big corporations are way too different in terms of benefits and negatives.
The fact that you did a good job in the big corp, doesn’t mean you will handle everyday fires at startups.
Do the research and make sure you understand what you have now, what you get and what you lose, in each choice. Unfortunately, there is no silver bullet.
17. Having a great manager is the best denominator to having a healthy work life
You probably already know that; if you have a supportive manager with whom you can communicate effectively and you like the tasks you have in your job it would be a blessing.
People don’t leave companies, people leave their managers.  Ideally, do so before accepting an offer.
18. The only way to make sure you know something is to teach it
That is the reason I initially started blogging.
If I learn about something interesting (even as part of my personal training plan or on the job), I try to explain via my blog (even though my Msc has led me to reduce the posting frequency).
I think it works very nice. At least for me, I have never asked my readers :)
19. If you stop learning you are already obsolete
The relatively high salaries that are connected with the software sector are correlated (among others) with the level of change that occurs in this field. The sector is moving so fast that if you stop learning you are already obsolete.
That doesn’t mean you should spend every waking hour reading and coding, but don’t go on the other extreme where you have settled and neglected the changes that are happening.
20. Consistency is better than intensity
Connected to your learning mentality above, reading about your craft 30-40 minutes per day is better than spending the whole Sunday in front of the monitor to cram unreasonable studying workloads.
You knew that from college, didnt you? Stress levels were way better when you did were doing your homework, bite by bite and not in the last evening.
21. First, make it work then make it right then make it fast
That famous expression from Kent Beck, is one of my (and Unix’s) favorite mottos. I am shocked by the lack of pragmatic thinking in the industry.
People giving too many fucks about spaces vs. tabs, about snake case vs camel case, about how to name an interface, etc. No one (?) disagrees that those are important things but they are important only if we have something that works.
My favorite, is watching people, putting extensive documentation to code that is not even compiling. Be pragmatic, friends.
22. If you want to spend time on your social media make sure it’s worth it
Follow great people who are worth your time. I follow many great people on Twitter including many co-authors from dev.to.
Even though I’m not extremely active on tweeting, I’m always happy to read their opinions even if I disagree, as they give a great perspective to me.
23. Don’t be afraid of asking questions
Regardless of your position in the company, maybe you’re the owner or the CTO, no sane human-being expects you to know everything. On the contrary, research shows that people tend to like you more when you ask them for their help.
Also, the field of computer science is so huge, that YOU CANNOT know everything. Just like in every other profession with huge levels of bibliography.
24. Theorems and brainstorming don’t pay the bills
Results do. Happy customers and successful products do. You are not in school anymore. No one cares about your romantic approach to coding,theories that are not any more applicable to industry problems or your what-ifs. I am not saying they are not important but the corporate world favors results and not abstract discussions.
It is a sad truth. If you don’t like that, maybe the corporate life is not for you. Prefer to find your shelter to an R&D oriented place or a university.
25. Try to have hobbies outside of software
I am a bit guilty about not following that advice lately.
Between a full-time job, fitness training and a master’s degree I can’t say I am proud of myself here. I am trying to fix it though.
It will help you avoiding burnout and get a different perspective on things.
For example, I have seen various articles around connecting enhanced diffuse mode (see above) of thinking with playing an instrument.
26. Do not be opinionated on tech
I don’t think this needs an explanation. There are various alternatives in software technologies, for a reason. Stop preaching a particular programming ideology or technology, by considering it the one and only solution.
27. Never accept socially unacceptable behavior in your workplace
Sexism, racism, bullying, favorability against your career, being unpaid while the boss is on vacations (I have experienced this personally). There are companies with serious, mature and professional people who will respect you. Go out and find them. Let the rest, enjoy their micro-universe where they feel superior and unique.
28. Unit testing is boring but…
It can be life-saving when things are going tough like the product is growing big very fast or a big scale refactoring is coming.

29. Effective time/task management is more important than you think
Effective time/task management is equally or more important than learning the newest and hottest tech.
Why? Because if you cannot use your skills to provide value on time, no one cares. If you feel you are not good in that area, start with GTD theory and 15 secrets of time management
30. Don’t forget your soft skills
Unless you are a freelancer accepting faceless bids or someone who delegates the sales and customer handling stuff to others, you need this.
We work with people all day and we need to know how to communicate effectively and speak a business acceoptable language, with respect to people not being identical to us.
It might not be a problem if all people had low soft skills competency, but as this is a bit dystopian and I asked you to be pragmatic above, you need to become better on this field too.
I would recommend three books on that matter, for starters: Soft skills How to win Friends & Influence People The charisma myth
Conclusion
Thank you for making it this far, I hope you enjoyed the above tips. If you have any tips that would be helpful to other readers, I would love to read them.


Tutorial Flow:
- Determining the problem category
- Performing detailed diagnosis of the problem
- Identifying the scope of the SQL statements involved
Determining the problem category
As part of the initial diagnosis, the database administrator looks for
familiar patterns of symptoms to categorize the problem at a high level.
This example uses the MON_GET_SERVICE_SUBCLASS table function.
Note: Set the refresh interval on the top right pane to 5 seconds.
Some metrics that indicate some general problem categories
- TOTAL_CPU_TIME (Higher than normal) :: High processor usage is a good indicator that the slowdown is located within data server processing. The data server is working harder than normal to handle application requests.
- CLIENT_IDLE_WAIT_TIME (Normal) :: The slowdown is NOT located in the client.
- TOTAL_WAIT_TIME (Normal) :: Requests are NOT spending more time waiting for resources needed such as locks or for input and output.
- TOTAL_SECTION_PROC_TIME (Normal) :: The execution of SQL statements (access plan sections) is NOT slowed down.
From this evidence, the database administrator is able to rule out some common explanations for performance problems. The data server is expending more resource to process requests and statement execution processing is not slowed down. The slowdown is occurring either before or after statement execution.


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A topic that show you the changes that we made on our homepage.
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07.02.2013
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* some more design changes to get in touch with new bf4 colors
* added email notifications for topics (on top of each topic there's a new button if you logged in)
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BrainFooLong
Administrator
21.06.2013
* added post preview before submit
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BrainFooLong
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24.06.2013
* added email notification for new private messages - can be activated in account settings
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BrainFooLong
Administrator
24.07.2013
* some bugfixes in pm and topic emails
* bugfix after topic post save, homepage is changing language randomly
* some minor improvements in the board
* avatar filesize limit added because some avatars are massivly overdosed
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24.07.2013
* also added gravatar for avatars - you can enable it in your account settings
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04.09.2013
* some forum recalibration (new subforums, threads moved, etc...)
If you see many unread threads that you've already read, just click on Menu -> Forums -> Mark all as read.
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26.09.2013
* added "spoiler" support to the forums - to better format big posts
Example:
Click me
Spoiled :)
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Important note: Today (at 13:12 UTC) i imported a backup from Today, 07:32 UTC. Because the database was corrupt. All data between this period is lost, sorry. It's just 6 hours but some posts was already made in this time.
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Cookie Reputation disabled because of massive protest.
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Rebuilt the bblog statistics engine from scratch.

Added Image Tag with Description to Post Editor.


ALERT_ALL
Alert for all the Notifications: Group and Children
See Also
NotificationCompat.GROUP_ALERT_ALL


| DOSE: T+ 0:00 |
13.5 mg |
oral |
5-MeO-DiPT |
(powder / crystals) |
| T+ 0:59 |
|
smoked |
Cannabis |
(plant material) |
After doing alot of research about 5-MeO-DiPT and obtaining some I decided to try it with 4 of my close friends. I'm a type 1 diabetic so I make sure to trip during a time I don't have to eat or take any insulin shots. Usually a good 6 to 12 of free time, hours such as from lunch time to dinner time or from about 8 or 9 at night to 8 or 9 the next morning. I also check my blood sugar several times during the course of the trip.
Two of the friends present had tried between 7-10mg the week previous (although I did not) and enjoyed the experience but said they would like to try more. I measure out 100mg using a scale accurate to .01g and then proceed to divide that into 15 equal piles. I spent a considerable amount of time 15-20 min doing this.
Saturday night at 7:30 I take 2 capsules containg a total of 12-15mg. Everyone else takes the same about. Cops comes on and we watch that for a half hour. I always find it ironic that they never show the suspect get away or overpower the officers. We then go into the living room to listen to some music and all agree we are feeling good, slight mood lift, somewhat shaky, legs wabbaly.  We get there still feeling good, nice body high but no visuals. After staying at the party awhile it is now 10:30 and about 2 hours since we took the initial dose, me and another friend contemplate boosting with snorting 6-7mg each, but decide against it which as it turns out is a good decison, which I will get into later.
At about 11:15 11:30 we began to get slight visuals, eyes dialated, people's faces seem different, some patterns appear more detailed, colors somewhat enhanced. Still able to talk and interact normally. Midnight leave party now on drive home notice slight tracers from my feet as I walk to my car. As I drive home I notice blue halos around street lights and slight waving of streets but nothing overwhelming just nice calm visuals. I get home and sit down on my front porch and notice patterns in the sidewalk and things slightly breathing and waving, and heavier trails.
Now from about 12:30-3:30 i'm tripping good I lay down and close my eyes and see some of the most beatiful closed eye visuals i've ever seen. Morphing shapes very detailed geometric images with burst of vibrating colors. At this point I begin to get worried I overdose because I thought the experience was supposed to last 4-6 hours and here I'm T+ 7hrs (2:30am) and it is more intense than it has been all night. I calm myself down and figure maybe it just took longer to get digested. I did eat dinner about an hour and a half before and a chocolate chip muffin about 20-30 after dosing. I really want to get to sleep but can't and finally at about 5:15-5:30 T+10hrs I drift off to sleep and wake up feeling a little fuzzy headed but awake and ready to got a 11am.
In conclusion I'm glad I didn't redose. I would recommend being very patient as the full effects didn't hit me until T+5hrs. So it was very easy to feel like I didn't take enough up until that point. Also I didn't experience any diarrha stomach discomfort. I though all in all it was a fun and worthwhile experience But I didn't like to long tail duration. I will next time plan better because it took almost 10hrs from when I took it to when I could finally sleep.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Rate of Convergence and Optimality Properties of Root Finding and Optimization Algorithms
By Arthur Ira Cohen
B.S. (Cornell University) 1965 M.S. (Cornell University) 1967
DISSERTATION
Submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY
in
Engineering in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY
Approved:
Committee in Charge
DEGREE CONFERRED SEPT. 18, 1970
eeeerreosneeseeeoeneeoene eee noe ee ee eee reese ee


Alan and Kaitlyn travel the world! From Thailand to Tasmania and Hobart to Hobbiton
For those of you who can't see the above video - there's a version with the song removed and some other random song inserted. Or for the nostalgic effect we aimed for - play your own version of "Waves" by Mr. Probz instead. Check out our full Fraser Island post…
One of the main things we wanted to do in Australia was visit Fraser Island. It's considered to be the largest sand island in the world and we got several recommendations for it before our trip. We debated whether we should do it or not because it was a lot…
KL was definitely a bit more expensive than the rest of our Southeast Asia trip, which we weren't really expecting. We had a hard time finding a place to stay that was good but not expensive; we ended up with one in Chinatown (right beside Petaling Street) that was not…
We tried once to visit Ninh Binh and failed. Originally we'd planned on visiting Halong Bay - it was one of the things I'd looked forward to for this trip, but as we looked into it more and more we weren't so sure. The trips are quite expensive, and most…
The main tourist area in Hanoi is the old quarter north of Hoàn Kiếm Lake. We found Hanoi in general to be a bit more expensive, as you might expect from a big city. We stayed at a nice place right by the centre of things just a half block…
We ended up just a little over our budget in Vietnam. As with Thailand, we budgeted $50 a day for the pair of us, and we ended up spending $55.42 per day. So a little over but not drastically so. Given that we had $100 budgeted for travel from…


You are given a pointer to the root of a binary tree; print the values in in-order traversal.

Input Format
You are given a function,
void Inorder(node *root) {
}
Output Format
Print the values on a single line separated by space.
Sample Input
3
/ \
5 2
/ \ /
1 4 6
Sample Output
1 5 4 3 6 2


--- layout: page title: About header-img: "img/about-bg.png" ---
Ron Lynch is an IT Manager, Project Manager, and aspiring Web Developer. After years of working as a Manager in different capacities, he decided to go for a career change, deciding to focus on the ever-expanding field of tech.


Why a backup is needed
Having a backup system is necessary to ensure continuity to your systems, whatever work they do.
Backup systems can be simple if few machines are involved or complex in case there are hundreds of thousands of machines, such as in the cloud.
What to choose
If your ecosystem includes a few machines, you will probably want to choose something simple .
A good and simple backup system is rsync. With rsync you can perform local backups (on external disks) or remote (on other machines on the network).
For example, it is possible to perform incremental backups in a simple and schedulable way.
$ rsync -avz /path/of/backup /mnt/bck/ # local backup
$ rsync -avz /path/of/backup username@remote_host:/bck # remote backup
However, not all needs have such a simple solution. For example, in some cases, it is also necessary to provide for the restore on a certain precise day.
In these cases, a more structured backup system is needed, which keeps track of the various backups performed for each machine. One of my favorite tools is rdiff-backup. To start, let’s make a backup:
$ rdiff-backup /path/of/backup username@remote_host::/bck
At this point we can specify a restore point.
$ rdiff-backup -r now username@remote_host::/bck /path/of/backup # restore at the current time
$ rdiff-backup -r 10D username@remote_host::/bck /path/of/backup # restore at 10 days ago
But what if 10 days ago, you had five backup points? I should go into the increments
directory and look at what was backed up in that specific incremental backup. This is frustrating if you have a hundred machines. At this point, a structured backup system is needed; each backup must be able to have a precise restore point.
Butterfly Backup
Butterfly Backup is a simple rsync wrapper, written in python.
The concept behind Butterfly Backup is flexibility. Butterfly Backup can be used to make backups to your PC, or to the NAS at home, or to a production storage, or why not, a hundred servers.
Butterfly Backup also has a catalog system for each backup performed. Each backup has useful information on the single backup, such as a unique identifier (uuid) that allows you to choose (and backup), multiple backups per day, every hour and so on.
Furthermore, Butterfly Backup has a backup parallelism system. By default, 5 simultaneous bulk backups can be performed, using all CPUs, to optimize backup speed.
This tool does not need special configurations. The only one, in case you want the clients under backup not to ask for the password, is the exchange of keys in the SSH protocol. In this case, you can use the config
options:
$ bb config --new
$ bb config --deploy remote_host
$ bb config --deploy remote_host2 --user root # different user specified
Butterfly Backup has two backup modes: single host or in bulk.
Depending on your needs, one or the other mode will populate the same catalog
.
Backup
To make a single or bulk backup, just do the following:
$ bb backup --computer remote_host --destination /mnt/backup --data User Config --type Unix # single backup
$ cat << EOF > /home/test/bck_list
remote_host
remote_host2
<IP_ADDRESS>
EOF
$ bb backup --list /home/test/bck_list --destination /mnt/backup --data User Config --type Unix # bulk backup
A look at the catalog
The catalog is available immediately after the first backup. It consists of a file inside the backup folder. The catalog syntax is a simple config file, but the tool provides a clearer interface for consulting backups. First, let’s look at what backups are available.
$ bb list --catalog /mnt/backup
At this point, we can choose a certain backup and look at the details.
$ bb list --catalog /mnt/backup --backup-id dd6de2f2-9a1e-11e8-82b0-005056a664e0
If we want to go deeper and understand what we have backed up, then we ask for details.
$ bb list --catalog /mnt/backup --detail dd6de2f2-9a1e-11e8-82b0-005056a664e0
$ bb list --catalog /mnt/backup --detail dd6de2f2-9a1e-11e8-82b0-005056a664e0 | grep *.pdf # all pdf files
Restore
Butterfly Backup has a restore system that allows you to recover a single backup point directly on the original client or you can restore the files on another computer.
$ bb restore --catalog /mnt/backup --backup-id dd6de2f2-9a1e-11e8-82b0-005056a664e0 --computer remote_host
Extra features
Butterfly Backup also has additional features such as:
-
backup retention, for days and number of backups
-
export backup on other file system path
-
archive policy, which compresses backups into zip files
-
four backup modes: Incremental, Differential, Full and Mirror
Automate backups
An easy way to automate Butterfly Backup is to enter commands in the crontab.
CMDBB="bb backup --list /home/test/bck_list --destination /mnt/backup --data User Config --type Unix"
echo "00 12 * * * $CMDBB" | crontab -
In this way, we have automated every day incremental backups of different clients, servers or other.
Conclusion
Butterfly Backup is a tool created to group all the rsync and rdiff-backup features, adding parallelism and retention in a single command, removing complexity at the presentation level.
Furthermore, Butterfly Backup natively has four types of backup modes, which allows it to be versatile on multiple situations, whether it is a backup of our homes or a backup of configurations under /etc
.
In just a few steps, we have implemented an enterprise backup system on a Unix based operating system.
For more information on this tool, follow the links below:
WEBSITE: Butterfly Backup
GITHUB: Butterfly Backup Github
DOCS: Read the docs
OPTHER ARTICLE: Fedora Magazine


tag and as the title value of social media cards
It defaults value of `title` in _config.yml
To override, add a `title` value to a page's frontmatter.
{% endcomment %}
{%- assign meta_title = page.meta_title
| default: page.title
| default: site.title
| liquify
| replace:'"', '"'
-%}
{% comment %}
This appears in the "description" meta tag and as the description value of
social media cards
It defaults value of `description` in _config.yml
To override, add a `description` value to a page's frontmatter.
{% endcomment %}
{%- assign meta_description = page.meta_description
| default: page.description
| default: site.description
| liquify
| strip_html
| strip_newlines
| truncate: 160
| replace:'"', '"'
-%}
{% comment %}
This is the name of a file found in _assets/img/meta/ which will be shown as
feature image in social media cards
It defaults value of `meta_image` in _config.yml
To override, add a `meta_image` value to a page's frontmatter.
{% endcomment %}
{%- assign meta_image = page.meta_image
| default: site.meta_image
| prepend: 'meta/'
| liquify
-%}
{% comment %}
This is the digested asset path of meta_image. This should not be overridden
{% endcomment %}
{%- capture meta_image_asset_url %}{{ site.url }}{{ assets[meta_image].digest_path }}{% endcapture -%}


The Hopetoun Falls is a waterfall across the Aire River that is located in The Otways region of Victoria, Australia.
Hopetoun Falls


DEP OptOut can be annoying and stop binaries from running
Adding your binary to:
HKLM\Software\Microsoft\Winodws NT\CurrentVersion\AppCompatFlag\Layers with the value "DisableNXShowUI" doesn't work
This does:
rundll32 sysdm.cpl, NoExecuteAddFileOptOutList "C:\temp\evil.exe"


Get your city's weather information with Awesome WeatherApp!! Create Account {% csrf_token %} {% if status %} {{ status }} {% endif %} Create Account Have an account? Log In


|
H8SDK
|
3694s I/O map [詳解]
マクロ定義 | |
| #define | _LVD (*(volatile struct _st_lvd *)0xF730) |
| LVD Address. [詳解] | |
| #define | _IIC2 (*(volatile struct _st_iic2 *)0xF748) |
| IIC2 Address. [詳解] | |
| #define | _TW (*(volatile struct _st_tw *)0xFF80) |
| TW Address. [詳解] | |
| #define | _FLASH (*(volatile struct _st_flash *)0xFF90) |
| FLASH Address. [詳解] | |
| #define | _TV (*(volatile struct _st_tv *)0xFFA0) |
| TV Address. [詳解] | |
| #define | _TA (*(volatile struct _st_ta *)0xFFA6) |
| TA Address. [詳解] | |
| #define | _SCI3 (*(volatile struct _st_sci3 *)0xFFA8) |
| SCI3 Address. [詳解] | |
| #define | _AD (*(volatile struct _st_ad *)0xFFB0) |
| A/D Address. [詳解] | |
| #define | _WDT (*(volatile struct _st_wdt *)0xFFC0) |
| WDT Address. [詳解] | |
| #define | _ABRK (*(volatile struct _st_abrk *)0xFFC8) |
| ABRK Address. [詳解] | |
| #define | _IO (*(volatile struct _st_io *)0xFFD0) |
| IO Address. [詳解] | |
| #define | _SYSCR1 (*(volatile union _un_syscr1*)0xFFF0) |
| SYSCR1 Address. [詳解] | |
| #define | _SYSCR2 (*(volatile union _un_syscr2*)0xFFF1) |
| SYSCR2Address. [詳解] | |
| #define | _IEGR1 (*(volatile union _un_iegr1 *)0xFFF2) |
| IEGR1 Address. [詳解] | |
| #define | _IEGR2 (*(volatile union _un_iegr2 *)0xFFF3) |
| IEGR2 Address. [詳解] | |
| #define | _IENR1 (*(volatile union _un_ienr1 *)0xFFF4) |
| IENR1 Address. [詳解] | |
| #define | _IRR1 (*(volatile union _un_irr1 *)0xFFF6) |
| IRR1 Address. [詳解] | |
| #define | _IWPR (*(volatile union _un_iwpr *)0xFFF8) |
| IWPR Address. [詳解] | |
| #define | _MSTCR1 (*(volatile union _un_mstcr1*)0xFFF9) |
| MSTCR1 Address. [詳解] | |
3694s I/O map
@version $Id:$ @author Kenji MINOURA /<EMAIL_ADDRESS>Copyright (c) 2008 K&J Software Design, Ltd All rights reserved.
3694s.h に定義があります。
| struct _st_lvd |
| struct _st_iic2 |
| struct _st_tw |
| struct _st_flash |
| struct _st_tv |
| struct _st_ta |
| struct _st_sci3 |
| struct _st_ad |
| struct _st_wdt |
| struct _st_abrk |
| struct _st_io |
| クラスメンバ | ||
|---|---|---|
| union _st_io | PUCR1 | |
| union _st_io | PUCR5 | |
| char | wk1[2] | |
| union _st_io | PDR1 | |
| union _st_io | PDR2 | |
| char | wk2[2] | |
| union _st_io | PDR5 | |
| char | wk3 | |
| union _st_io | PDR7 | |
| union _st_io | PDR8 | |
| char | wk4 | |
| union _st_io | PDRB | |
| char | wk5[2] | |
| union _st_io | PMR1 | |
| union _st_io | PMR5 | |
| char | wk6[2] | |
| unsigned char | PCR1 | |
| unsigned char | PCR2 | |
| char | wk7[2] | |
| unsigned char | PCR5 | |
| char | wk8 | |
| unsigned char | PCR7 | |
| unsigned char | PCR8 |
| union _un_syscr1 |
| union _un_syscr2 |
| union _un_iegr1 |
| union _un_iegr2 |
| union _un_ienr1 |
| union _un_irr1 |
| union _un_iwpr |
| union _un_mstcr1 |
| union _st_lvd.CR |
| struct _st_lvd.CR.BIT |
| union _st_lvd.SR |
| struct _st_lvd.SR.BIT |
| union _st_iic2.ICCR1 |
| struct _st_iic2.ICCR1.BIT |
| union _st_iic2.ICCR2 |
| struct _st_iic2.ICCR2.BIT |
| union _st_iic2.ICMR |
| struct _st_iic2.ICMR.BIT |
| union _st_iic2.ICIER |
| struct _st_iic2.ICIER.BIT |
| union _st_iic2.ICSR |
| struct _st_iic2.ICSR.BIT |
| union _st_iic2.SAR |
| struct _st_iic2.SAR.BIT |
| union _st_tw.TMRW |
| struct _st_tw.TMRW.BIT |
| union _st_tw.TCRW |
| struct _st_tw.TCRW.BIT |
| union _st_tw.TIERW |
| struct _st_tw.TIERW.BIT |
| union _st_tw.TSRW |
| struct _st_tw.TSRW.BIT |
| union _st_tw.TIOR0 |
| struct _st_tw.TIOR0.BIT |
| union _st_tw.TIOR1 |
| struct _st_tw.TIOR1.BIT |
| union _st_flash.FLMCR1 |
| struct _st_flash.FLMCR1.BIT |
| union _st_flash.FLMCR2 |
| struct _st_flash.FLMCR2.BIT |
| union _st_flash.FLPWCR |
| struct _st_flash.FLPWCR.BIT |
| union _st_flash.EBR1 |
| struct _st_flash.EBR1.BIT |
| union _st_flash.FENR |
| struct _st_flash.FENR.BIT |
| union _st_tv.TCRV0 |
| struct _st_tv.TCRV0.BIT |
| union _st_tv.TCSRV |
| struct _st_tv.TCSRV.BIT |
| union _st_tv.TCRV1 |
| struct _st_tv.TCRV1.BIT |
| union _st_ta.TMA |
| struct _st_ta.TMA.BIT |
| union _st_sci3.SMR |
| struct _st_sci3.SMR.BIT |
| struct _st_sci3.SCR3.BIT |
| struct _st_sci3.SSR.BIT |
| union _st_ad.ADCSR |
| struct _st_ad.ADCSR.BIT |
| union _st_wdt.TCSRWD |
| struct _st_wdt.TCSRWD.BIT |
| union _st_wdt.TMWD |
| struct _st_wdt.TMWD.BIT |
| union _st_abrk.CR |
| struct _st_abrk.CR.BIT |
| union _st_abrk.SR |
| struct _st_abrk.SR.BIT |
| union _st_io.PUCR1 |
| struct _st_io.PUCR1.BIT |
| union _st_io.PUCR5 |
| struct _st_io.PUCR5.BIT |
| union _st_io.PDR1 |
| struct _st_io.PDR1.BIT |
| union _st_io.PDR2 |
| struct _st_io.PDR2.BIT |
| union _st_io.PDR5 |
| struct _st_io.PDR5.BIT |
| union _st_io.PDR7 |
| struct _st_io.PDR7.BIT |
| union _st_io.PDR8 |
| struct _st_io.PDR8.BIT |
| union _st_io.PDRB |
| struct _st_io.PDRB.BIT |
| union _st_io.PMR1 |
| struct _st_io.PMR1.BIT |
| union _st_io.PMR5 |
| struct _st_io.PMR5.BIT |
| struct _un_syscr1.BIT |
| struct _un_syscr2.BIT |
| struct _un_iegr1.BIT |
| struct _un_iegr2.BIT |
| struct _un_ienr1.BIT |
| struct _un_irr1.BIT |
| struct _un_iwpr.BIT |


It was dark, very dark, and I was running very fast. A loud noise, I heard it behind of me. What should I do? I thought to myself.
Click here to keep running.
Click here to NOT keep running.
Click here to hide.


| # | Display Image | Name | Location | View Profile | Invite for interview | |
|---|---|---|---|---|---|---|
| 1 | Bill Sheng |<EMAIL_ADDRESS>| Toronto | View | Invite for Interview | |
| 2 | Rahib Kanjiyani |<EMAIL_ADDRESS>| Toronto | View | Invite for Interview | |
| 3 | Richard Xie |<EMAIL_ADDRESS>| Toronto | View | Invite for Interview |


黒の手番です。52 はどう動かしますか？


pusion.core.combiner¶
- class pusion.core.combiner.Combiner¶
Bases:
object
Combiner’s root class. This class as well as the subclasses in this module set the structure for each combiner provided by the framework. It also accommodates methods and attributes which are essential for combiner’s registration and obtainment.
Each combiner to be registered in the framework, needs to base at least one of the following classes:
Furthermore, it needs a list
_SUPPORTED_PAC
of supported PAC tuples set as a class attribute. A PAC tuple is a tuple of string constants (classification problem, assignment type and coverage type). See:pusion.util.constants.Problem
pusion.util.constants.AssignmentType
pusion.util.constants.CoverageType
respectively. Example of a new combiner:
class NewCombiner(TrainableCombiner): _SUPPORTED_PAC = [ (Problem.MULTI_CLASS, AssignmentType.CRISP, CoverageType.REDUNDANT), (Problem.MULTI_CLASS, AssignmentType.CONTINUOUS, CoverageType.REDUNDANT) ] def train(self, decision_outputs, true_assignments): pass def combine(self, decision_outputs): pass
Warning
Note that a new combiner also needs to be inserted into the
pusion.Method
class within pusion.__init__.py file.- classmethod obtain(config)¶
Obtain a combiner registered by the framework.
- Parameters
config –
pusion.model.configuration.Configuration
. User-defined configuration.- Returns
A combiner object.
- abstract combine(decision_tensor)¶
Abstract method. Combine decision outputs by combiner’s implementation.
- Parameters
decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of decision outputs by different classifiers per sample.
- Returns
numpy.array of shape (n_samples, n_classes). A matrix of class assignments which represents fused decisions obtained by combiner’s implementation. Axis 0 represents samples and axis 1 the class assignments which are aligned with axis 2 in
decision_tensor
input tensor.
- set_coverage(coverage)¶
Set the coverage for complementary-redundant decisions.
- Parameters
coverage – list of list elements. Each inner list contains classes as integers covered by a classifier, which is identified by the positional index of the respective list.
- class pusion.core.combiner.UtilityBasedCombiner¶
Bases:
pusion.core.combiner.Combiner
A combiner of type
UtilityBasedCombiner
fuses decisions solely based on the outputs of the ensemble classifiers. It does not take any further information or evidence about respective ensemble classifiers into account.- abstract combine(decision_tensor)¶
Abstract method. Combine decision outputs by combiner’s implementation.
- Parameters
decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of decision outputs by different classifiers per sample.
- Returns
numpy.array of shape (n_samples, n_classes). A matrix of class assignments which represents fused decisions obtained by combiner’s implementation. Axis 0 represents samples and axis 1 the class assignments which are aligned with axis 2 in
decision_tensor
input tensor.
- class pusion.core.combiner.TrainableCombiner¶
Bases:
pusion.core.combiner.Combiner
A combiner of type
TrainableCombiner
needs to be trained using decision outputs of the ensemble classifiers with true class assignments in order to combine decisions of unknown samples.- abstract combine(decision_tensor)¶
Abstract method. Combine decision outputs by combiner’s implementation.
- Parameters
decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of decision outputs by different classifiers per sample.
- Returns
numpy.array of shape (n_samples, n_classes). A matrix of class assignments which represents fused decisions obtained by combiner’s implementation. Axis 0 represents samples and axis 1 the class assignments which are aligned with axis 2 in
decision_tensor
input tensor.
- abstract train(decision_tensor, true_assignments)¶
Abstract method. Train combiner’s implementation using decision outputs an appropriate true assignments.
- Parameters
decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of decision outputs by different classifiers per sample.
true_assignments – numpy.array of shape (n_samples, n_classes). Matrix of class assignments which are considered true for each sample during the training procedure.
- class pusion.core.combiner.EvidenceBasedCombiner¶
Bases:
pusion.core.combiner.Combiner
A combiner of type
EvidenceBasedCombiner
takes an additional evidence into account while combining outputs of ensemble classifiers. Thus, it is able to empower better classifiers in order to obtain a fusion result with higher overall classification performance.- abstract combine(decision_tensor)¶
Abstract method. Combine decision outputs by combiner’s implementation.
- Parameters
decision_tensor – numpy.array of shape (n_classifiers, n_samples, n_classes). Tensor of decision outputs by different classifiers per sample.
- Returns
numpy.array of shape (n_samples, n_classes). A matrix of class assignments which represents fused decisions obtained by combiner’s implementation. Axis 0 represents samples and axis 1 the class assignments which are aligned with axis 2 in
decision_tensor
input tensor.
- abstract set_evidence(evidence)¶
Abstract method. Set the evidence for evidence-based combiner implementations. The evidence is given by confusion matrices calculated according to Kuncheva 1.
- 1
Ludmila I Kuncheva. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014.
- Parameters
evidence – list of numpy.array elements of shape (n_classes, n_classes). Confusion matrices for each ensemble classifier.


- Details
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Published: 02 May 2014
It doesn’t matter if you are a small business or a large corporation. There are always benefits that can be gained by outsourcing your software design and development processes. The practice of using an outside firm isn’t unfamiliar to the industry. In fact, companies frequently outsource things like accounting, distribution and other functions to cut costs. But, most companies forget that they can also outsource their software design and development.
You Can Focus on Core Functions
Handling development and design in-house means you will have to dedicate your resources and staff to that process. That doesn’t leave much for anything else — including core functions inside your business. By outsourcing the project, you can refocus on company activities that serve your business immediately.
It’s Cost Effective
Software design and development is complicated in nature. Whether it is the size of your company preventing you from handling it in-house or just the lack of talent, outsourcing is often more cost-effective than trying to boost up your staff to handle it in-house.
Reduces Your Overhead
The cost to hire the right staff, buy the equipment and then maintain it all is extensive. If you’re just developing one software program, these are extreme costs for your company to endure for a single project. By outsourcing to a software design and development firm, you can reduce your overhead burden and make your project more profitable in the end.
You Get Trained, Qualified and Certified Professionals
In-house development requires you to not only find the most qualified professionals, but to make sure they’re certified, trained and capable of handling your project. This is an excessive burden that most companies cannot handle. But, when you outsource to professional design and development firms, you’re already hiring a team of highly skilled, qualified and certified professionals.
New Technology is Quickly Taken Advantage Of
Outsourcing to a skilled design and development firm means they will already have the resources to start new projects right away. Taking on the same job in-house might require several weeks or even months for you to hire everyone you need, train them, get the equipment required, etc.
Risk is Reduced
Every investment and project in your company is a risk. With the government regulations, markets and competition, you know that the industry could change any moment. By outsourcing to a design and development provider, all of the risk is managed for you. You’re dealing with professionals that know how to avoid these risks and who are working in their area of expertise.
Compliance and Security Are Increased
Do you have all of your software, firewalls and PCI equipment up to date? You might not, but the company you outsource to will. It is their job to remain compliant at all times and provide maximum security for the projects they take on – which means you won’t have to worry about complying with any new security regulations.
Neubloc offers beneficial, cost-effective outsourcing to companies that need to develop their software programs but cannot handle the burden in-house. Learn more about how Neubloc can help your company by contacting a Neubloc representative.


State of Rhode Island
Department of Health
To track drug overdoses, to assist health and social service agencies, public and private, as well as healthcare providers, in the treatment and rehabilitation of people who use drugs.
Cause of death attributed to accidental death drug overdoses, the use of naloxone by emergency medical technicians.
The overdose data webpage is the process of being restructured. Information from additional data sources will be added to the site. Emergency Medical Services (EMS) and Emergency Department (ED) data will be provided on nonfatal drug overdoses, and information from the State Unintentional Drug Overdose Reporting System (SUDORS) will be added on fatal drug overdoses. We anticipate these data to be available by June 2019.
Policy on Identifying and Reporting Confirmed Accidental Drug-Related Overdose Deaths
On April 17, 2019, one additional fatal drug overdose case was identified that occurred in 2018. As of 4/17/2019, tables and graphs on demographics and fentanyl were removed. They will be made available on the restructured data page. Information through 2018 is still accessible via the data table.


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layout: post
title: ! 'RT @extralife: Jim Henson died...'
tags:
- tumble
- tweets
- twitter
status: publish
type: post
published: true
meta:
aktt_notify_twitter: 'no'
aktt_twitter_id: '70402960995258369'
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RT @extralife: Jim Henson died 21 years ago. The world is less great with out him. http://www.youtube.com/watch?v=olHV1o9TE-8