In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save the last 21 days
test_data = data[-21*24:]
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.input_nodes)) #2x56
"""
print("Weights - Input->Hidden: ", self.weights_input_to_hidden.shape)
"""
self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5,
(self.output_nodes, self.hidden_nodes)) #1x2
"""print("Weights - Hidden->Output: ", self.weights_hidden_to_output.shape)
"""
self.lr = learning_rate
#### Set this to your implemented sigmoid function ####
# Activation function is the sigmoid function
# TODO: Activation Function
# ADDED
#self.activation_function = sigmoid if defined as a new function
self.activation_function = lambda x: 1 / (1 + np.exp(-x))
def train(self, inputs_list, targets_list):
# Convert inputs list to 2d array
inputs = np.array(inputs_list, ndmin=2).T # 56x1
targets = np.array(targets_list, ndmin=2).T # 1x1
"""
print("Inputs: ", inputs.shape)
print("Targets: ", targets.shape)
"""
#### Implement the forward pass here ####
### Forward pass ###
# TODO: Hidden layer
# ADDED
hidden_inputs = inputs # signals into hidden layer
hidden_outputs = self.activation_function(np.dot(self.weights_input_to_hidden, inputs)) # signals from hidden layer
"""
print(hidden_inputs.shape) #56x1
print(hidden_outputs.shape) #2x1
"""
# TODO: Output layer
# ADDED
# signals into final output layer
final_inputs = hidden_outputs #2x1
# signals from final output layer
# NOTE: NO SIGMOID !!!!!!!!!!!!!!
final_outputs = np.dot(self.weights_hidden_to_output, final_inputs) #1x1
"""
print(final_inputs.shape)
print(final_outputs.shape)
"""
#### Implement the backward pass here ####
### Backward pass ###
# TODO: Output error
# ADDED
output_errors = targets - final_outputs # 1x1 - Output layer error is the difference between desired target and actual output.
# error gradient for output layer
# NOTE: NO SIGMOID !!!!!!!!!!!!!!
# del_error_outputs = output_errors * final_outputs * (1 - final_outputs)
del_error_outputs = output_errors
#### CONTINUE ####
#### CONTINUE ####
#### CONTINUE ####
# TODO: Backpropagated error
# hidden layer gradients
# hidden_grad = output_errors * final_outputs * (1 - final_outputs) #1x1 * 1x1 = 1x1
# errors propagated to the hidden layer
hidden_errors = del_error_outputs * final_inputs * (1 - final_inputs) * self.weights_hidden_to_output.T #1x1 * 1x2 * 2x1 = 1
"""
print(hidden_grad.shape)
print(hidden_errors.shape)
"""
# TODO: Update the weights
# ADDED
# update hidden-to-output weights with gradient descent step
self.weights_hidden_to_output += self.lr * del_error_outputs * hidden_outputs.T #1x1 * 2x1 = 1x1
# update input-to-hidden weights with gradient descent step
self.weights_input_to_hidden += self.lr * hidden_errors * inputs.T
def run(self, inputs_list):
# Run a forward pass through the network
inputs = np.array(inputs_list, ndmin=2).T
#### Implement the forward pass here ####
# TODO: Hidden layer
# ADDED (as above)
hidden_inputs = inputs # signals into hidden layer
hidden_outputs = self.activation_function(np.dot(self.weights_input_to_hidden, inputs)) # signals from hidden layer
# TODO: Output layer
# ADDED (as above)
# signals into final output layer
final_inputs = hidden_outputs
# signals from final output layer
# NOTE: NO SIGMOID !!!!!!!!!!!!!!
final_outputs = np.dot(self.weights_hidden_to_output, final_inputs)
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### TODO: Set the hyperparameters here ###
epochs = 400
learning_rate = 0.1
hidden_nodes = 20
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for e in range(epochs):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
for record, target in zip(train_features.ix[batch].values,
train_targets.ix[batch]['cnt']):
network.train(record, target)
# Printing out the training progress
train_loss = MSE(network.run(train_features), train_targets['cnt'].values)
val_loss = MSE(network.run(val_features), val_targets['cnt'].values)
sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim(ymax=0.5)
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features)*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.
import unittest
inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3],
[-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328, -0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, 0.39775194, -0.29887597],
[-0.20185996, 0.50074398, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
print(network.run(inputs))
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)


RTM
`Prev `__
R
`Next `__
--------------
**RTM**: /R·T·M/
1. [Usenet: abbreviation for ‘Read The Manual’] Politer variant of
`*RTFM* `__.
2. Robert Tappan Morris, perpetrator of the great Internet worm of 1988
(see `*Great Worm* <../G/Great-Worm.html>`__); villain to many, naive
hacker gone wrong to a few. Morris claimed that the worm that brought
the Internet to its knees was a benign experiment that got out of
control as the result of a coding error. After the storm of negative
publicity that followed this blunder, Morris's username on ITS was
hacked from RTM to `*RTFM* `__.
--------------
+------------------------+----------------------------+------------------------+
| `Prev `__ | `Up <../R.html>`__ | `Next `__ |
+------------------------+----------------------------+------------------------+
| RTI | `Home <../index.html>`__ | RTS |
+------------------------+----------------------------+------------------------+


There's only one step left before you can start using fireplace. Please verify your email address by clicking the button below.
If you received this email by mistake, you can simply ignore it.


| Class and Description |
|---|
| FriendsList
FriendsList is an object that holds a series of usernames
which are indicated to be friends of a certain user,
and provides an easy way to access and mutate this data
|
| InventoryList
InventoryList is the object that holds an array of vehicles
that belong to a specific user.
|
| NotificationManager
Created by adrianomarini on 2015-11-04.
|
| Photo
Photo is the enclosed object that implements
Bitmap functionality to create, set, return,
and delete images from an item
|
| SlidingTabLayout.TabColorizer
Allows complete control over the colors drawn in the tab layout.
|
| Trade
Trade is the major object to encapsulate all
information about a trade that will occur
All relevant information to the trade will be held here.
|
| TradeList
Created by adrianomarini on 2015-10-26.
|
| User
This Class stores all necessary information pertaining to the user for storage and access.
|
| UserSingleton
UserSingleton is the current instance of user.
|
| Vehicle
Vehicle stores all information regarding the vehicles in the app.
|
| VehicleCategory
An enumerator that represents all possible categories a vehicle can be.
|
| VehicleQuality
An enumerator that represents all possible qualities a vehicle can be
|


--- layout: default ---
I'm a software developer with data analysis skills who hails from a humanities and legal background. I feel this amalgamation of skills makes me an interesting and capable developer. I'm interested in backend web development, data analysis, machine learning, and data visualization. I am currently looking for exciting opportunities in the Research Triangle Area of North Carolina. Please feel free to download my resume and contact me for any comments and questions.
The four projects below highlight my skill at programming in Python, creating web applications, building REST APIs, and performing data analysis.
Bringing Buzzwords Home is a three person data visualization project created by myself, Jonathan Kross, and David Conley while we were students at The Iron Yard. We extracted, transformed, and loaded (ETL) data from disparate sources into relational database, fed visualizations via dynamically created JSON, and created NVD3 visualizations from that JSON. You may view the code for this project on GitHub.
Tools used include: Git, Django, Django Rest Framework, Postgresql, D3, NVD3 and deployment via Heroku
The Computer Programming Language Predictor is python script that utilizes machine learning to predict the programming language (out of a set of 15) of any given program.
Tools used include: Git, Scikit-Learn, and Jupyter Notebook
Analysis of the American Time Use Survey (ATUS) is a Jupyter Notebook and python module that do exploratory data analysis of parts of the ATUS.
monty_hall.py is a python script that executes a Monte Carlo simulation to prove the "switch strategy" to the Monty Hall Problem.


# TODO
An indefinite project Structure. The Structure should include and indexed readme, pointing to the proper location of sub-readme in the project. The track sheets and executions sheets should be bound accordingly.
The aim is to increase the community to make a tool available to everyone that can make sense of the vast information available through internet and surface it in a human readable sized content.
- Language Folder
- Bootstrap Script to quickly check the current setup of the system and what else will be required to run the next command
- List of all the services. Possible with the current Machine architecture and installation. Since there is a possibility that some of the applications might require different set of package. IT should be clearly mentioned which versions to be installed and of what. But the whole setup/troubleshooting should be done using single command.
- Commands and chronological setup of tasks possible
- Aiming at commiting some scrappers in plain JAVA or Python Language


Greggs customer hits out at staff member over 'banter about how much she visits'
Kathy Readon says she was told by a worker at a branch in Alton, Hants, 'you're in here every day' - but says she can't afford to eat at Greggs that regularly
by Jonny WilkinsonA Greggs customer has hit out at a staff member over his 'banter' about how much she visits.
Kathy Readon says she was told by an employee "you're in here every day, you're going to get sick of this eventually" as she bought a pizza at the branch in Alton, Hants.
However the 21-year-old failed to see the funny side and responded that she couldn't afford to eat there every day - but claims the man continued his joke during the incident on Wednesday, Mirror.co.uk reports.
Kathy took to Facebook to demand an explanation from the snack giant as to why she had received an apparent 'dressing down' for being a regular.
Following her complaint, Kathy says Greggs issued her an apology, a £6.00 voucher and said that 'things are going to be done' as the staff member's conduct had been flagged to the area manager.
She says that due to anxiety, the staff member's joke was like her "worst fears all happening all at once".
Kathy, from Guildford, said: "Literally all that happened was I went in - I am a regular there, I work down the road and have been doing so for a year.
"I'm very anxious, I don't really talk to people. But I went in, I ordered what I normally order and I think he was trying to be banterous with me but it did not come off that way.
"He essentially just made fun of me for ordering pizza and it kind of felt like he was making fun of unhealthy habits.
"It came across like he was picking on me. There were other people in the store and he kept saying that I was in there every single day.
"I wish I had the income that would allow me to be at Greggs every single day but I don't.
"When I said this he argued with me about it. During the interaction I also told him he was making me feel very anxious and he ignored it.
"He carried on speaking to me in the same manner, saying the same things.

"I have lots of mental health things so I fear public interaction on a daily basis. I'm always worried that something like that is going to happen. I invent it in my head.
"But then it did happen in real life and it was like my worst fears all happening all at once."
Kathy, an officer worker in Alton, said Greggs issued her an apology and a £6.00 voucher and said that 'things are going to be done'.
She said: "Greggs have said they're very sorry and that this is not the behaviour they expect.
"They said that they're going to let their area manager know and things are going to be done and apparently I will notice improvements, which is nice. To be honest, I'm quite happy with Greggs response."
Mirror Online has contacted Greggs for comment.


Manu Bhaker Wins Gold In Shooting World Cup
Manu Bhaker won gold in the women's 10m air pistol event.
by Press Trust of IndiaIndia's Manu Bhaker fired her way to the gold medal with a junior world record in the women's 10m air pistol event of the World Cup Finals in China's Putian on Thursday. The 17-year-old Bhaker shot 244.7 to claim the top prize in the prestigious season-ending tournament of International Shooting Sport Federation (ISSF). Her compatriot, Yashaswini Singh Deswal finished sixth in the final of the event.
Serbia's Zorana Arunovic won the silver with 241.9, while Quian Wang of China bagged the bronze with 221.8.
In the men's 10m air pistol event, Abhishek Verma and Saurabh Chaudhary qualified for the final. Verma topped the qualifications with 588 while Chaudhary was seventh with 581.


# Sat Mar 20 18:34:54 2021 Copyright (C) 1994-2020 Synopsys, Inc. This Synopsys software and all associated documentation are proprietary to Synopsys, Inc. and may only be used pursuant to the terms and conditions of a written license agreement with Synopsys, Inc. All other use, reproduction, modification, or distribution of the Synopsys software or the associated documentation is strictly prohibited. Tool: Synplify Pro (R) Build: Q-2020.03M-SP1 Install: C:\Microsemi\Libero_SoC_v12.6\SynplifyPro OS: Windows 6.2 Hostname: DESKTOP-V8NANO1 Implementation : rev_1 Synopsys Generic Technology Mapper, Version map202003act, Build 160R, Built Oct 22 2020 12:05:41, @ Mapper Startup Complete (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 119MB peak: 119MB) @N:MF916 : | Option synthesis_strategy=base is enabled. @N:MF248 : | Running in 64-bit mode. @N:MF667 : | Clock conversion disabled. (Command "set_option -fix_gated_and_generated_clocks 0" in the project file.) Design Input Complete (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 121MB peak: 130MB) Mapper Initialization Complete (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 121MB peak: 130MB) Start loading timing files (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 123MB peak: 130MB) Finished loading timing files (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 125MB peak: 130MB) Starting Optimization and Mapping (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 160MB peak: 160MB) Available hyper_sources - for debug and ip models None Found Finished RTL optimizations (Real Time elapsed 0h:00m:00s; CPU Time elapsed 0h:00m:00s; Memory used current: 164MB peak: 164MB) Encoding state machine RD_CTRL1.PS[0:1] (in view: work.mia_simple_fifo_synch(rtl)) original code -> new code 00 -> 0 01 -> 1 @N:MO225 : readcontroller.vhd(29) | There are no possible illegal states for state machine RD_CTRL1.PS[0:1] (in view: work.mia_simple_fifo_synch(rtl)); safe FSM implementation is not required. @N:FX403 : sdpram.vhd(25) | Property "block_ram" or "no_rw_check" found for RAM SDPRAM.ram_block[15:0] with specified coding style. Inferring block RAM. @W:FX107 : sdpram.vhd(25) | RAM SDPRAM.ram_block[15:0] (in view: work.mia_simple_fifo_synch(rtl)) does not have a read/write conflict check. Possible simulation mismatch. To resolve a read/write conflict, either set syn_ramstyle = rw_check, or enable the "Read Write Check on RAM" Implementation Option. For more information, search for "read/write conflict check" in Online Help. @A:BN291 : sdpram.vhd(39) | Boundary register SDPRAM.q[15:0] (in view: work.mia_simple_fifo_synch(rtl)) is packed into a complex cell. To disable register packing, set syn_keep=1 on the net between the register and the complex cell. @N:MF179 : design.vhd(86) | Found 16 by 16 bit equality operator ('==') un1_Mux_in_1 (in view: work.mia_simple_fifo_synch(rtl)) Encoding state machine PS[0:1] (in view: work.WR_Ctrl(rtl)) original code -> new code 00 -> 0 01 -> 1 @N:MO225 : writecontroller.vhd(30) | There are no possible illegal states for state machine PS[0:1] (in view: work.WR_Ctrl(rtl)); safe FSM implementation is not required. Starting factoring (Real Time elapsed 0h:00m:01s; CPU Time elapsed 0h:00m:01s; Memory used current: 166MB peak: 166MB) Finished factoring (Real Time elapsed 0h:00m:01s; CPU Time elapsed 0h:00m:01s; Memory used current: 166MB peak: 166MB) Finished generic timing optimizations - Pass 1 (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:01s; Memory used current: 166MB peak: 166MB) Starting Early Timing Optimization (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:01s; Memory used current: 166MB peak: 167MB) Finished Early Timing Optimization (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:01s; Memory used current: 167MB peak: 167MB) Finished generic timing optimizations - Pass 2 (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:02s; Memory used current: 167MB peak: 167MB) Finished preparing to map (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:02s; Memory used current: 167MB peak: 167MB) Finished technology mapping (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:02s; Memory used current: 167MB peak: 167MB) Pass CPU time Worst Slack Luts / Registers ------------------------------------------------------------ 1 0h:00m:02s 1.92ns 15 / 27 @N:FP130 : | Promoting Net rd_clk_c on CLKINT I_13 @N:FP130 : | Promoting Net wr_clk_c on CLKINT I_14 Added 0 Buffers Added 0 Cells via replication Added 0 Sequential Cells via replication Added 0 Combinational Cells via replication Finished technology timing optimizations and critical path resynthesis (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:02s; Memory used current: 168MB peak: 168MB) Finished restoring hierarchy (Real Time elapsed 0h:00m:02s; CPU Time elapsed 0h:00m:02s; Memory used current: 168MB peak: 168MB) @S |Clock Optimization Summary #### START OF CLOCK OPTIMIZATION REPORT #####[ Clock optimization not enabled 2 non-gated/non-generated clock tree(s) driving 46 clock pin(s) of sequential element(s) 0 gated/generated clock tree(s) driving 0 clock pin(s) of sequential element(s) 0 instances converted, 0 sequential instances remain driven by gated/generated clocks =================================== Non-Gated/Non-Generated Clocks =================================== Clock Tree ID Driving Element Drive Element Type Fanout Sample Instance ------------------------------------------------------------------------------------------------------ ClockId0001 rd_clk port 40 FF_Out.S ClockId0002 wr_clk port 6 SDPRAM.ram_block_ram_block_0_0 ====================================================================================================== ##### END OF CLOCK OPTIMIZATION REPORT ######] Start Writing Netlists (Real Time elapsed 0h:00m:03s; CPU Time elapsed 0h:00m:02s; Memory used current: 140MB peak: 168MB) Writing Analyst data base C:\Users\rafac\Desktop\MasterClue\Curso\7_Module_Embedded_Neural_Networks\FPGA\Proyecto\Github\HomeWork3_Part3\rev_1\synwork\Implementation_m.srm Finished Writing Netlist Databases (Real Time elapsed 0h:00m:03s; CPU Time elapsed 0h:00m:02s; Memory used current: 168MB peak: 168MB) Writing Verilog Simulation files @N:BW103 : | The default time unit for the Synopsys Constraint File (SDC or FDC) is 1ns. @N:BW107 : | Synopsys Constraint File capacitance units using default value of 1pF Finished Writing Verilog Simulation files (Real Time elapsed 0h:00m:04s; CPU Time elapsed 0h:00m:03s; Memory used current: 169MB peak: 169MB) Start final timing analysis (Real Time elapsed 0h:00m:04s; CPU Time elapsed 0h:00m:03s; Memory used current: 169MB peak: 170MB) @W:MT246 : design.vhd(75) | Blackbox FSM_Address is missing a user supplied timing model. This may have a negative effect on timing analysis and optimizations (Quality of Results) @W:MT420 : | Found inferred clock mia_simple_fifo_synch|rd_clk with period 6.67ns. Please declare a user-defined clock on port rd_clk. @W:MT420 : | Found inferred clock mia_simple_fifo_synch|wr_clk with period 6.67ns. Please declare a user-defined clock on port wr_clk. ##### START OF TIMING REPORT #####[ # Timing report written on Sat Mar 20 18:34:58 2021 # Top view: mia_simple_fifo_synch Requested Frequency: 150.0 MHz Wire load mode: top Paths requested: 5 Constraint File(s): @N:MT320 : | This timing report is an estimate of place and route data. For final timing results, use the FPGA vendor place and route report. @N:MT322 : | Clock constraints include only register-to-register paths associated with each individual clock. Performance Summary ******************* Worst slack in design: 1.763 Requested Estimated Requested Estimated Clock Clock Starting Clock Frequency Frequency Period Period Slack Type Group ------------------------------------------------------------------------------------------------------------------------------------ mia_simple_fifo_synch|rd_clk 150.0 MHz 203.9 MHz 6.667 4.904 1.763 inferred Inferred_clkgroup_0 mia_simple_fifo_synch|wr_clk 150.0 MHz 476.0 MHz 6.667 2.101 4.566 inferred Inferred_clkgroup_1 System 150.0 MHz 1062.7 MHz 6.667 0.941 5.726 system system_clkgroup ==================================================================================================================================== Clock Relationships ******************* Clocks | rise to rise | fall to fall | rise to fall | fall to rise -------------------------------------------------------------------------------------------------------------------------------------------------- Starting Ending | constraint slack | constraint slack | constraint slack | constraint slack -------------------------------------------------------------------------------------------------------------------------------------------------- System mia_simple_fifo_synch|rd_clk | 6.667 5.768 | No paths - | No paths - | No paths - System mia_simple_fifo_synch|wr_clk | 6.667 5.726 | No paths - | No paths - | No paths - mia_simple_fifo_synch|rd_clk System | 6.667 5.441 | No paths - | No paths - | No paths - mia_simple_fifo_synch|rd_clk mia_simple_fifo_synch|rd_clk | 6.667 1.763 | No paths - | No paths - | No paths - mia_simple_fifo_synch|rd_clk mia_simple_fifo_synch|wr_clk | Diff grp - | No paths - | No paths - | No paths - mia_simple_fifo_synch|wr_clk System | 6.667 5.100 | No paths - | No paths - | No paths - mia_simple_fifo_synch|wr_clk mia_simple_fifo_synch|rd_clk | Diff grp - | No paths - | No paths - | No paths - mia_simple_fifo_synch|wr_clk mia_simple_fifo_synch|wr_clk | 6.667 4.566 | No paths - | No paths - | No paths - ================================================================================================================================================== Note: 'No paths' indicates there are no paths in the design for that pair of clock edges. 'Diff grp' indicates that paths exist but the starting clock and ending clock are in different clock groups. Interface Information ********************* No IO constraint found ==================================== Detailed Report for Clock: mia_simple_fifo_synch|rd_clk ==================================== Starting Points with Worst Slack ******************************** Starting Arrival Instance Reference Type Pin Net Time Slack Clock ----------------------------------------------------------------------------------------------------------------------------------------------------- SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[0] ram_block_ram_block_0_0_NEWA[0] 2.263 1.763 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[2] ram_block_ram_block_0_0_NEWA[2] 2.263 1.777 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[4] ram_block_ram_block_0_0_NEWA[4] 2.263 1.792 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[6] ram_block_ram_block_0_0_NEWA[6] 2.263 1.806 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[8] ram_block_ram_block_0_0_NEWA[8] 2.263 1.821 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[10] ram_block_ram_block_0_0_NEWA[10] 2.263 1.836 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[12] ram_block_ram_block_0_0_NEWA[12] 2.263 1.850 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[14] ram_block_ram_block_0_0_NEWA[14] 2.263 1.865 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[1] ram_block_ram_block_0_0_NEWA[1] 2.263 2.133 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|rd_clk RAM1K18 A_DOUT[3] ram_block_ram_block_0_0_NEWA[3] 2.263 2.148 ===================================================================================================================================================== Ending Points with Worst Slack ****************************** Starting Required Instance Reference Type Pin Net Time Slack Clock ------------------------------------------------------------------------------------------------------------------------------- FF_Out.S mia_simple_fifo_synch|rd_clk SLE D un1_Mux_in_1_0_data_tmp[7] 6.411 1.763 Registers\.11\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[11] 6.411 2.619 Registers\.4\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[4] 6.411 2.619 Registers\.1\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[1] 6.411 2.619 Registers\.5\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[5] 6.411 2.619 Registers\.7\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[7] 6.411 2.619 Registers\.6\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[6] 6.411 2.619 Registers\.12\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[12] 6.411 2.619 Registers\.3\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[3] 6.411 2.619 Registers\.9\.FF_Out.S mia_simple_fifo_synch|rd_clk SLE D rd_data_c[9] 6.411 2.619 =============================================================================================================================== Worst Path Information View Worst Path in Analyst *********************** Path information for path number 1: Requested Period: 6.667 - Setup time: 0.255 + Clock delay at ending point: 0.000 (ideal) = Required time: 6.411 - Propagation time: 4.649 - Clock delay at starting point: 0.000 (ideal) = Slack (critical) : 1.763 Number of logic level(s): 9 Starting point: SDPRAM.ram_block_ram_block_0_0 / A_DOUT[0] Ending point: FF_Out.S / D The start point is clocked by mia_simple_fifo_synch|rd_clk [rising] (rise=0.000 fall=3.333 period=6.667) on pin A_CLK The end point is clocked by mia_simple_fifo_synch|rd_clk [rising] (rise=0.000 fall=3.333 period=6.667) on pin CLK Instance / Net Pin Pin Arrival No. of Name Type Name Dir Delay Time Fan Out(s) --------------------------------------------------------------------------------------------------------------------- SDPRAM.ram_block_ram_block_0_0 RAM1K18 A_DOUT[0] Out 2.263 2.263 r - ram_block_ram_block_0_0_NEWA[0] Net - - 0.248 - 1 SDPRAM.ram_block_ram_block_0_0_OLDA_RNIBDRG[0] CFG3 B In - 2.511 r - SDPRAM.ram_block_ram_block_0_0_OLDA_RNIBDRG[0] CFG3 Y Out 0.165 2.676 r - rd_data_c[0] Net - - 1.117 - 4 un1_Mux_in_1_0_I_1 ARI1 D In - 3.793 r - un1_Mux_in_1_0_I_1 ARI1 FCO Out 0.505 4.298 r - un1_Mux_in_1_0_data_tmp[0] Net - - 0.000 - 1 un1_Mux_in_1_0_I_9 ARI1 FCI In - 4.298 r - un1_Mux_in_1_0_I_9 ARI1 FCO Out 0.015 4.313 r - un1_Mux_in_1_0_data_tmp[1] Net - - 0.000 - 1 un1_Mux_in_1_0_I_15 ARI1 FCI In - 4.313 r - un1_Mux_in_1_0_I_15 ARI1 FCO Out 0.015 4.327 r - un1_Mux_in_1_0_data_tmp[2] Net - - 0.000 - 1 un1_Mux_in_1_0_I_27 ARI1 FCI In - 4.327 r - un1_Mux_in_1_0_I_27 ARI1 FCO Out 0.015 4.342 r - un1_Mux_in_1_0_data_tmp[3] Net - - 0.000 - 1 un1_Mux_in_1_0_I_45 ARI1 FCI In - 4.342 r - un1_Mux_in_1_0_I_45 ARI1 FCO Out 0.015 4.356 r - un1_Mux_in_1_0_data_tmp[4] Net - - 0.000 - 1 un1_Mux_in_1_0_I_33 ARI1 FCI In - 4.356 r - un1_Mux_in_1_0_I_33 ARI1 FCO Out 0.015 4.371 r - un1_Mux_in_1_0_data_tmp[5] Net - - 0.000 - 1 un1_Mux_in_1_0_I_39 ARI1 FCI In - 4.371 r - un1_Mux_in_1_0_I_39 ARI1 FCO Out 0.015 4.386 r - un1_Mux_in_1_0_data_tmp[6] Net - - 0.000 - 1 un1_Mux_in_1_0_I_21 ARI1 FCI In - 4.386 r - un1_Mux_in_1_0_I_21 ARI1 FCO Out 0.015 4.400 r - un1_Mux_in_1_0_data_tmp[7] Net - - 0.248 - 1 FF_Out.S SLE D In - 4.649 r - ===================================================================================================================== Total path delay (propagation time + setup) of 4.904 is 3.290(67.1%) logic and 1.614(32.9%) route. Path delay compensated for clock skew. Clock skew is added to clock-to-out value, and is subtracted from setup time value ==================================== Detailed Report for Clock: mia_simple_fifo_synch|wr_clk ==================================== Starting Points with Worst Slack ******************************** Starting Arrival Instance Reference Type Pin Net Time Slack Clock -------------------------------------------------------------------------------------------------------------------- WR_CTRL1.PS[0] mia_simple_fifo_synch|wr_clk SLE Q PS[0] 0.108 4.566 Write_RST_Sinchroniser.FF2.S mia_simple_fifo_synch|wr_clk SLE Q wr_rst_sync 0.108 4.838 Read_Bit_Sinchroniser.FF2.S mia_simple_fifo_synch|wr_clk SLE Q rd_bit 0.108 5.658 Read_Bit_Sinchroniser.FF1.S mia_simple_fifo_synch|wr_clk SLE Q S 0.087 5.750 Write_RST_Sinchroniser.FF1.S mia_simple_fifo_synch|wr_clk SLE Q S 0.087 5.750 ==================================================================================================================== Ending Points with Worst Slack ****************************** Starting Required Instance Reference Type Pin Net Time Slack Clock ----------------------------------------------------------------------------------------------------------------------------------- SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|wr_clk RAM1K18 B_WEN[0] wr_busy_c 6.133 4.566 SDPRAM.ram_block_ram_block_0_0 mia_simple_fifo_synch|wr_clk RAM1K18 B_WEN[1] wr_busy_c 6.133 4.566 WR_CTRL1.PS[0] mia_simple_fifo_synch|wr_clk SLE D PS_nsss[0] 6.411 4.838 Write_FSM mia_simple_fifo_synch|wr_clk FSM_Address enable wr_busy_c 6.667 5.100 Write_FSM mia_simple_fifo_synch|wr_clk FSM_Address rst wr_rst_sync 6.667 5.441 Read_Bit_Sinchroniser.FF2.S mia_simple_fifo_synch|wr_clk SLE D S_0 6.411 5.750 Write_RST_Sinchroniser.FF2.S mia_simple_fifo_synch|wr_clk SLE D S_0_2 6.411 5.750 =================================================================================================================================== Worst Path Information View Worst Path in Analyst *********************** Path information for path number 1: Requested Period: 6.667 - Setup time: 0.534 + Clock delay at ending point: 0.000 (ideal) = Required time: 6.133 - Propagation time: 1.567 - Clock delay at starting point: 0.000 (ideal) = Slack (non-critical) : 4.566 Number of logic level(s): 1 Starting point: WR_CTRL1.PS[0] / Q Ending point: SDPRAM.ram_block_ram_block_0_0 / B_WEN[0] The start point is clocked by mia_simple_fifo_synch|wr_clk [rising] (rise=0.000 fall=3.333 period=6.667) on pin CLK The end point is clocked by mia_simple_fifo_synch|wr_clk [rising] (rise=0.000 fall=3.333 period=6.667) on pin B_CLK Instance / Net Pin Pin Arrival No. of Name Type Name Dir Delay Time Fan Out(s) ---------------------------------------------------------------------------------------------------- WR_CTRL1.PS[0] SLE Q Out 0.108 0.108 f - PS[0] Net - - 0.248 - 1 WR_CTRL1.we_out CFG2 A In - 0.357 f - WR_CTRL1.we_out CFG2 Y Out 0.087 0.444 f - wr_busy_c Net - - 1.123 - 6 SDPRAM.ram_block_ram_block_0_0 RAM1K18 B_WEN[0] In - 1.567 f - ==================================================================================================== Total path delay (propagation time + setup) of 2.101 is 0.729(34.7%) logic and 1.371(65.3%) route. Path delay compensated for clock skew. Clock skew is added to clock-to-out value, and is subtracted from setup time value ==================================== Detailed Report for Clock: System ==================================== Starting Points with Worst Slack ******************************** Starting Arrival Instance Reference Type Pin Net Time Slack Clock -------------------------------------------------------------------------------------------------- Write_FSM System FSM_Address address_out[0] wr_address[0] 0.000 5.726 Write_FSM System FSM_Address address_out[1] wr_address[1] 0.000 5.726 Write_FSM System FSM_Address address_out[2] wr_address[2] 0.000 5.726 Write_FSM System FSM_Address address_out[3] wr_address[3] 0.000 5.726 Write_FSM System FSM_Address address_out[4] wr_address[4] 0.000 5.726 Write_FSM System FSM_Address address_out[5] wr_address[5] 0.000 5.726 Write_FSM System FSM_Address address_out[6] wr_address[6] 0.000 5.726 Write_FSM System FSM_Address address_out[7] wr_address[7] 0.000 5.726 Write_FSM System FSM_Address address_out[8] wr_address[8] 0.000 5.726 Write_FSM System FSM_Address address_out[9] wr_address[9] 0.000 5.726 ================================================================================================== Ending Points with Worst Slack ****************************** Starting Required Instance Reference Type Pin Net Time Slack Clock ---------------------------------------------------------------------------------------------------------------- SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[4] wr_address[0] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[5] wr_address[1] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[6] wr_address[2] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[7] wr_address[3] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[8] wr_address[4] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[9] wr_address[5] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[10] wr_address[6] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[11] wr_address[7] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[12] wr_address[8] 6.036 5.726 SDPRAM.ram_block_ram_block_0_0 System RAM1K18 B_ADDR[13] wr_address[9] 6.036 5.726 ================================================================================================================ Worst Path Information View Worst Path in Analyst *********************** Path information for path number 1: Requested Period: 6.667 - Setup time: 0.631 + Clock delay at ending point: 0.000 (ideal) = Required time: 6.036 - Propagation time: 0.310 - Clock delay at starting point: 0.000 (ideal) - Estimated clock delay at start point: -0.000 = Slack (non-critical) : 5.726 Number of logic level(s): 0 Starting point: Write_FSM / address_out[0] Ending point: SDPRAM.ram_block_ram_block_0_0 / B_ADDR[4] The start point is clocked by System [rising] The end point is clocked by mia_simple_fifo_synch|wr_clk [rising] (rise=0.000 fall=3.333 period=6.667) on pin B_CLK Instance / Net Pin Pin Arrival No. of Name Type Name Dir Delay Time Fan Out(s) -------------------------------------------------------------------------------------------------------------- Write_FSM FSM_Address address_out[0] Out 0.000 0.000 r - wr_address[0] Net - - 0.310 - 1 SDPRAM.ram_block_ram_block_0_0 RAM1K18 B_ADDR[4] In - 0.310 r - ============================================================================================================== Total path delay (propagation time + setup) of 0.941 is 0.631(67.0%) logic and 0.310(33.0%) route. Path delay compensated for clock skew. Clock skew is added to clock-to-out value, and is subtracted from setup time value ##### END OF TIMING REPORT #####] Timing exceptions that could not be applied Finished final timing analysis (Real Time elapsed 0h:00m:04s; CPU Time elapsed 0h:00m:03s; Memory used current: 169MB peak: 170MB) Finished timing report (Real Time elapsed 0h:00m:04s; CPU Time elapsed 0h:00m:03s; Memory used current: 169MB peak: 170MB) --------------------------------------- Resource Usage Report for mia_simple_fifo_synch Mapping to part: m2s050tvf400std Cell usage: CLKINT 2 uses FSM_Address 2 uses CFG2 7 uses CFG3 16 uses Carry cells: ARI1 8 uses - used for arithmetic functions Sequential Cells: SLE 44 uses DSP Blocks: 0 of 72 (0%) I/O ports: 39 I/O primitives: 39 INBUF 21 uses OUTBUF 18 uses Global Clock Buffers: 2 RAM/ROM usage summary Total Block RAMs (RAM1K18) : 1 of 69 (1%) Total LUTs: 31 Extra resources required for RAM and MACC interface logic during P&R: RAM64x18 Interface Logic : SLEs = 0; LUTs = 0; RAM1K18 Interface Logic : SLEs = 36; LUTs = 36; MACC Interface Logic : SLEs = 0; LUTs = 0; Total number of SLEs after P&R: 44 + 0 + 36 + 0 = 80; Total number of LUTs after P&R: 31 + 0 + 36 + 0 = 67; Mapper successful! At Mapper Exit (Real Time elapsed 0h:00m:04s; CPU Time elapsed 0h:00m:03s; Memory used current: 62MB peak: 170MB) Process took 0h:00m:05s realtime, 0h:00m:03s cputime # Sat Mar 20 18:34:59 2021 ###########################################################]


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There's something very soothing to me about putting together basic structures in a program.
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There's something very soothing to me about putting together basic structures in a program.
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Last Updated: 20 Nov 2020
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Last Updated: 20 Nov 2020
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Last Updated: 20 Nov 2020
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions


Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
Esterase and protease activity of purified guinea pig basophil granules.
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Receptor tyrosine kinase involved in the development and the maturation of the central and peripheral nervous systems through regulation of proliferation, differentiation and survival of sympathetic and nervous neurons. High affinity receptor for NGF which is its primary ligand (PubMed:1850821, PubMed:1849459, PubMed:1281417, PubMed:8325889, PubMed:15488758, PubMed:22649032, PubMed:17196528, PubMed:27445338). Can also bind and be activated by NTF3/neurotrophin-3. However, NTF3 only supports axonal extension through NTRK1 but has no effect on neuron survival (By similarity). Upon dimeric NGF ligand-binding, undergoes homodimerization, autophosphorylation and activation (PubMed:1281417). Recruits, phosphorylates and/or activates several downstream effectors including SHC1, FRS2, SH2B1, SH2B2 and PLCG1 that regulate distinct overlapping signaling cascades driving cell survival and differentiation. Through SHC1 and FRS2 activates a GRB2-Ras-MAPK cascade that regulates cell differentiation and survival. Through PLCG1 controls NF-Kappa-B activation and the transcription of genes involved in cell survival. Through SHC1 and SH2B1 controls a Ras-PI3 kinase-AKT1 signaling cascade that is also regulating survival. In absence of ligand and activation, may promote cell death, making the survival of neurons dependent on trophic factors.
We identified TrkA, a neurotrophin receptor tyrosine kinase, as a TRAF4-targeted ubiquitination substrate that promotes prostate cancer cell invasion, and found that inhibition of TrkA activity abolished TRAF4-dependent cell invasion.
NTRK1 gene fusion occurs in primary and metastatic melanomas.
Report a novel subset of high-grade uterine sarcomas with features reminiscent of soft tissue fibrosarcoma and characterized by oncogenic activation of Trk through recurrent NTRK gene fusions.
A cross-sectional study was performed in which the expression of TrkA, TrkB, nerve growth factor (NGF), and brain-derived neurotrophic factor (BDNF) was analyzed by immunohistochemistry of 154 melanoma samples. We investigated NT/TRK expression associations with prognostic factors for melanoma, relapse-free survival (RFS), and overall survival (OS).
A cross-sectional study was performed in which the expression of TrkA, TrkB, nerve growth factor (NGF), and brain-derived neurotrophic factor (BDNF) was analyzed by immunohistochemistry of 154 melanoma samples. We investigated NT/TRK expression associations with prognostic factors for melanoma, relapse-free survival (RFS), and overall survival (OS).
We identified TrkA, a neurotrophin receptor tyrosine kinase, as a TRAF4-targeted ubiquitination substrate that promotes prostate cancer cell invasion, and found that inhibition of TrkA activity abolished TRAF4-dependent cell invasion.


Various pre-packaged services are "sysinit" services, started as a result of being wanted by the "sysinit" target (either directly or indirectly via a targets such as "local-fs").
The machine-id service in the sysinit services collection is an example of a sysinit service.
Its start
, stop
, and restart
programs are all fairly straightforward and self-explanatory.
And its ready_after_exit
flag file causes the service to be considered ready only when its run
program has exited.
This is because the service is a "single-shot" service, considered "ready" when its run
program has done its work.
#!/bin/nosh #Set up the machine ID move-to-control-group ../machine-id.service envdir env setup-machine-id
The run
program runs the setup-machine-id
utility.
This utility performs boot-time actions that set up the /etc/machine-id
and /run/machine-id
files amongst others.
Most "sysinit" services are single-shot services that do their work and then exit. Only a few, such as the log services and the plug and play manager service, have long-lived dæmon processes.
"sysinit" services live in /etc/service-bundles/services
.
This is because "sysinit" services are part of, or precede, bringing up local filesystems; loaded in parallel with (or even before) mounting /usr
and /var
and re-mounting /
read-write.
For the same reasons, the supervise directories for "sysinit" services live under /run
(more specifically usually /run/service-bundles/early-supervise
), because they must be writable before the filesystem containing /etc
is remounted read-write.
The service manager is told to load them, and thus needs to create their control/status API files, first thing in the bootstrap procedure.
With the exception of the plug and play manager service, sysinit services all use a fan-in model of log service plumbing. The plug and play manager is a long-running service and has its own dedicated log service. Most other sysinit services are single-shot services that do not have long-running processes, and their log outputs all fan-in to a single "sysinit-log" logging service.
The logging services have to come up after the filesystem containing /var/log
is available and writable.
Log data from the logged services sits in its input pipe until then.
The consequence of this is that too much log output from sysinit services has the potential to deadlock the system.
(Too much output from the plug and play manager service simply halts it until its log service comes up.
The plug and play manager usually isn't in the critical path for mounting /var/log
read-write.)
So it is very important to minimize the amount of output generated by sysinit services.


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Charlotte and Jakob's Celebration Countdown
A long time ago, in a city college far far away in Pasadena, two nerds met in an engineering club and eventually became friends. Some time later, after one shared class, innumerable CAPS-LOCKED texts, a garage metal concert in a stranger's house, tearful heart-to-hearts, and a generous amount of watermelon vodka, those two nerds eventually figured things out and leveled up their friendship.
Fast forward five years to the present, they still like each other a lot and figure they could share four score years or more together in their shared weirdness.
Those two people are actually us (Charlotte and Jakob), if you haven't figured that out yet. What we're trying to say is that we're gonna have a marriage ceremony.
On Saturday March 31st, 2018 our honored guests are cordially invited to a ceremony that will bind our two families together by the will of a higher power: the US government.
The ceremony will be held in the beautifully maintained Club at Five Knolls ™ at 20551 Bentwood Ln, Santa Clarita, CA. The ceremony will start at 4 pm in the afternoon, barring any unforseen inclement weather such as car-sized hail, shark tornadoes, and light sprinkles.
If the spirit is willing, the party can last all night, but since we have to go to bed on time to watch Jeopardy re-runs, it'll most likely end by 10pm.
We are honored you will share in our special day. Your presence is our gift! We're lucky to already have a home full of everything we need, so please enjoy browsing this wish list, where you can contribute to our future and help make our dreams come true. Thanks for visiting and we can't wait to see you on our big day!
Thanks for being so generous!
The theme of the wedding is FUN! There will be lots of activities for everyone and it'll be better if you can move around freely. We encourage bright bold colors and any videogame/cartoon inspired clothing. Or even a costume if you'd like. If you insist on wearing formal clothing, you'll be given something in keeping with the theme. If you'd like a visual cue for what to wear, check out this pinterest board!
Can I bring my dog/cat/iguana?While we would love to have all creatures big and small attend our wedding, the venue is sadly not equipped to handle Princess Poochie. Plus why would you traumatize them with loud and drunk humans?
Can I bring a plus one?We wanted to invite everyone we knew, but the venue can only hold so many bodies (75 to be exact). We'd like our closest family and friends there so we've invited people we know. We can make judgment calls of your plus one though, so call or text us at<PHONE_NUMBER> (Charlotte) or<PHONE_NUMBER> (Jakob).
Can I throw my friend/mom/kid brother/grandma in the pool?Of course. The Club has a pool and jacuzzi so feel free to bring swim clothes and other things you might need to splash around.
Can I talk politics?


File
class
File extends SplFileInfo
A file in the file system.
Methods
public
|
|
__construct(string $path, bool $checkPath = true)
|
Constructs a new file from the given path. |
|
public
|
string|null |
guessExtension()
|
Returns the extension based on the mime type. |
|
public
|
string|null |
getMimeType()
|
Returns the mime type of the file. |
|
public
|
File |
move(string $directory, string $name = null)
|
Moves the file to a new location. |
|
protected
|
|
getTargetFile($directory, $name = null)
|
No description
|
|
protected
|
string |
getName(string $name)
|
Returns locale independent base name of the given path. |
|
Details
__construct()
public __construct(string $path, bool $checkPath = true)
Constructs a new file from the given path.
Parameters
string |
$path |
The path to the file |
bool |
$checkPath |
Whether to check the path or not |
Exceptions
| FileNotFoundException |
If the given path is not a file |
guessExtension()
public string|null guessExtension()
Returns the extension based on the mime type.
If the mime type is unknown, returns null.
This method uses the mime type as guessed by getMimeType()
to guess the file extension.
Return Value
string|null |
The guessed extension or null if it cannot be guessed |
See also
| ExtensionGuesser |
|
| getMimeType() |
|
getMimeType()
public string|null getMimeType()
Returns the mime type of the file.
The mime type is guessed using a MimeTypeGuesser instance, which uses finfo(),
mime_content_type() and the system binary "file" (in this order), depending on
which of those are available.
Return Value
string|null |
The guessed mime type (e.g. "application/pdf") |
See also
move()
public File move(string $directory, string $name = null)
Moves the file to a new location.
Parameters
string |
$directory |
The destination folder |
string |
$name |
The new file name |
Return Value
File |
A File object representing the new file |
Exceptions
| FileException |
if the target file could not be created |
getTargetFile()
protected getTargetFile($directory, $name = null)
getName()
protected string getName(string $name)
Returns locale independent base name of the given path.
Parameters
string |
$name |
The new file name |
Return Value
Source code
<?php
/*
* This file is part of the Symfony package.
*
* (c) Fabien Potencier<EMAIL_ADDRESS>*
* For the full copyright and license information, please view the LICENSE
* file that was distributed with this source code.
*/
namespace App\Khan\Component\HttpFoundation\File;
use App\Khan\Component\HttpFoundation\File\Exception\FileException;
use App\Khan\Component\HttpFoundation\File\Exception\FileNotFoundException;
use App\Khan\Component\HttpFoundation\File\MimeType\MimeTypeGuesser;
use App\Khan\Component\HttpFoundation\File\MimeType\ExtensionGuesser;
/**
* A file in the file system.
*
* @author Bernhard Schussek<EMAIL_ADDRESS>*/
class File extends \SplFileInfo
{
/**
* Constructs a new file from the given path.
*
* @param string $path The path to the file
* @param bool $checkPath Whether to check the path or not
*
* @throws FileNotFoundException If the given path is not a file
*/
public function __construct(string $path, bool $checkPath = true)
{
if ($checkPath && !is_file($path)) {
throw new FileNotFoundException($path);
}
parent::__construct($path);
}
/**
* Returns the extension based on the mime type.
*
* If the mime type is unknown, returns null.
*
* This method uses the mime type as guessed by getMimeType()
* to guess the file extension.
*
* @return string|null The guessed extension or null if it cannot be guessed
*
* @see ExtensionGuesser
* @see getMimeType()
*/
public function guessExtension()
{
$type = $this->getMimeType();
$guesser = ExtensionGuesser::getInstance();
return $guesser->guess($type);
}
/**
* Returns the mime type of the file.
*
* The mime type is guessed using a MimeTypeGuesser instance, which uses finfo(),
* mime_content_type() and the system binary "file" (in this order), depending on
* which of those are available.
*
* @return string|null The guessed mime type (e.g. "application/pdf")
*
* @see MimeTypeGuesser
*/
public function getMimeType()
{
$guesser = MimeTypeGuesser::getInstance();
return $guesser->guess($this->getPathname());
}
/**
* Moves the file to a new location.
*
* @param string $directory The destination folder
* @param string $name The new file name
*
* @return self A File object representing the new file
*
* @throws FileException if the target file could not be created
*/
public function move($directory, $name = null)
{
$target = $this->getTargetFile($directory, $name);
if (!@rename($this->getPathname(), $target)) {
$error = error_get_last();
throw new FileException(sprintf('Could not move the file "%s" to "%s" (%s)', $this->getPathname(), $target, strip_tags($error['message'])));
}
@chmod($target, 0666 & ~umask());
return $target;
}
protected function getTargetFile($directory, $name = null)
{
if (!is_dir($directory)) {
if (false === @mkdir($directory, 0777, true) && !is_dir($directory)) {
throw new FileException(sprintf('Unable to create the "%s" directory', $directory));
}
} elseif (!is_writable($directory)) {
throw new FileException(sprintf('Unable to write in the "%s" directory', $directory));
}
$target = rtrim($directory, '/\\').DIRECTORY_SEPARATOR.(null === $name ? $this->getBasename() : $this->getName($name));
return new self($target, false);
}
/**
* Returns locale independent base name of the given path.
*
* @param string $name The new file name
*
* @return string containing
*/
protected function getName($name)
{
$originalName = str_replace('\\', '/', $name);
$pos = strrpos($originalName, '/');
$originalName = false === $pos ? $originalName : substr($originalName, $pos + 1);
return $originalName;
}
}


1.What learning did you take from the chapter?
Emotional intelligence impacts many different aspects of our daily life, such as the way we behave and the way you interact with others. The important outcome of EI is reducing stress, remaining focused, and staying connected to yourself and others.It can help us to navigate the social complexities of the workplace, lead and motivate others, and excel in your career.On the other hand it can be acquired and improved with practice.
2.How did you find the two minutes of mindfulness?
The mindfulness is all about our mind. I found that it can be gradually developed using meditational practices. The major part of our failure and success come from our mind. Mind is the one direct us to do all things. Being clear mind is important for implementing succesful things. So meditation help us to concentrate and help to be in a default mindset, that is happiness.Apart from that meditation alleviate a variety of mental and physical conditions, such as bringing about reductions in depression symptoms, reducing stress,anxiety,and in the treatment of drug addiction.


Hi! This is our package for fitting densities using Dynamic Infinite Mixture of Normals models with Dirichlet Processes. It uses C++ Armadillo and Rcpp for maximum speed. We are especially interested in applications to spatial data. Stay tuned for updates!
In the mean time, here are some links to examples
| Topic | Thumbnail |
|---|---|
| 1. Documentation Home | |
| 2. Introduction | |
| 3. Dynamic Fitting | |
| 4. Spatial Austin Data |


0 volume as nobody wants to sell at such current prices and many are dealing with KMD refunds to be able to buy up the cheap ARRR
p.11-Jun-19 02:16 PM
Where is best place to buy ARRR
jl777c11-Jun-19 02:17 PM
with KMD, digitalprice has the most liquidity
Malti11-Jun-19 02:20 PM
0 volume solved
just bought some more(edited)
hehe
dukeleto11-Jun-19 02:52 PM
Does anybody know how long withdraws have been disabled on DP?
KMD:
Withdrawal temporary disabled for this coin
Deposits have been disabled for maintenance
jl777c11-Jun-19 02:52 PM
next week it will be enabled
dukeleto11-Jun-19 02:53 PM
It's my own fault for not checking, but I am angry it let me deposit and didn't tell me
I never leave my coins on an exchange more than a few hours. This is my own fault, and yet, as a DP customer, I feel that it's very scammy and shady
tolken11-Jun-19 03:10 PM
How does a cex prevent one from depositing? You mean there was no warning that deposits wouldn’t be accessible?
nael5511-Jun-19 04:09 PM
they can't stop you from "depositing", but they can make it so the deposit doesn't show up/isnt' spendable from your login
a few weeks ago binance disabled deposits but you could still send to the wallet and the deposit would credit
private guy11-Jun-19 04:11 PM
Does anyone know when coinexchange.io will allow trading again for Komodo assets? They’re in maintenance, since shortly after the vulnerability discovery.
nael5511-Jun-19 04:13 PM
don't hold your breath for CE
ComputerGenie 👴👴👴11-Jun-19 04:13 PM
maybe the people in the BTC trading discord know
private guy11-Jun-19 04:13 PM
@dukeleto what exchange is DP an abbreviation for? can’t be double penetration, although that’s probably how it feels
1
2
nael5511-Jun-19 04:14 PM
it's actually dong penetration
but close
p.11-Jun-19 04:14 PM
Dark Pentecostal
ComputerGenie 👴👴👴11-Jun-19 04:14 PM
DP is the initials for whatever their coin name is
p.11-Jun-19 04:15 PM
DP STANDS FOR DIGITAL PRICE.io
ComputerGenie 👴👴👴11-Jun-19 04:15 PM
yeah, that
private guy11-Jun-19 04:15 PM
thanks, although Dong felt right. Is Phrasing a thing here?
ComputerGenie 👴👴👴11-Jun-19 04:16 PM
although Dong felt right.
that's called "an over-share"
@Cryptojunkie4 You would lose to Justin Bieber. Tom Cruise would not.
3
Keppie11-Jun-19 09:16 PM
Binance down to 42btc volume
Cryptoon11-Jun-19 09:23 PM
Silence before the storm
tolken11-Jun-19 09:28 PM
Shitstorm?
Alecandro11-Jun-19 09:34 PM
yea I have a good feeling about KMD
just a feeling though haha
markuk11-Jun-19 10:56 PM
Anyone know a good site to buy bitcoin with low fees
Deleted User11-Jun-19 10:57 PM
well...not less fees
@markuk
which payment u using ?
markuk11-Jun-19 10:57 PM
pounds uk
Deleted User11-Jun-19 10:59 PM
Localbitcoins is good one
p.11-Jun-19 10:59 PM
Trump just tweeted : "$Komodo"
4
3
Deleted User11-Jun-19 10:59 PM
post it @p.
Hollowman900012-Jun-19 01:39 AM
@MadMaX were you going to be sending notary dividends to all of the compromised addresses that voted for you?
Alexfrog12-Jun-19 01:58 AM
lol good point
i guess notaries that pay rewards for voters need to get info on where to send
Alecandro12-Jun-19 04:08 AM
How is it that trading volume is so low on KMD??
jl777c12-Jun-19 04:08 AM
nobody wants to sell at this price
Alecandro12-Jun-19 05:26 AM
How is there no buy volume either though??
Keppie12-Jun-19 07:05 AM
If anyone wants to arbitrage. There's about 8k kmd on sale on DP for 0.00019500
5% arbi at current rates
Malti12-Jun-19 08:09 AM
btc retesting 8k
Cryptoon12-Jun-19 09:08 AM
@Keppie you cant withdraw from dp, and cant deposit on other sites, so arbi not really possible atm
Keppie12-Jun-19 09:10 AM
long term arbi
Markus12-Jun-19 10:31 AM
or u sell on binance and buy on DP and keep them
1
btc profit
hyperDEX12-Jun-19 10:32 AM
in a year 20k sats would be crazy cheap
1
klein12-Jun-19 11:03 AM
long term arbitrage
1
Malti12-Jun-19 11:44 AM
For many kinds of work there are three phases:
1. When nothing's working.
2. When it's working, but only your initial users/audience realize that.
3. When everyone knows it works.
Phase 2 is the most fun. Enjoy it while it lasts, because you'll miss it in phase 3.
And what about people who have already paid all their taxes and are just saving for the rainy day? This is robbery in broad daylight!


🔥 Experimental React Flare Updates
React Flare (the current name for the internal event API experiments) got a lot more updates this week. Check out this umbrella ticket for an overview of the current status.
Note: This idea is currently in early experimentation phase and might be discarded altogether.


WITNESS LIST Land & Resource Management Committee August 25, 2004-8:00A Interim Charge #2 For: Baker, Martin (Galveston County Erosion Task Force) Johnson, John (Jefferson County) Mohn, Jerry (West Galveston Island Property Owners Association) Neal, Loyd (City of Corpus Christi) Neblett, Georgia (City of Port Aransas) Pearson, Todd (City of Rockport) Pickett, Ellis (Surfrider Foundation Texas Chapter) Pinkerton, Robert (Town of South Padre Island) On: Fisher, Eddie (Texas General Land Office) Laine, Larry (Texas General Land Office) Sebiee, Ben (Texas Oil and Gas Association)


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public interface UniversalHash
General interface for perfect universal hash. Every class in this family should implement this interface. A universal hash function is a family of hash functions (as in the sense of hash functions for data structures) with the property that a randomly chosen hash function (from the family) yields very few collisions, with good probability. More importantly in a cryptographic context, universal hash functions have important properties, like good randomness extraction and pairwise independence. Many universal families are known (for hashing integers, vectors, strings), and their evaluation is often very efficient. The notions of perfect universal hashing and collision resistance hash are distinct, and should not be confused (it is unfortunate that they have a similar name). We therefore completely separate the two implementations so that collision-resistant hash functions cannot be confused with perfect universal hash functions.
| Method Summary | |
|---|---|
void |
compute(byte[] in,
int inOffset,
int inLen,
byte[] out,
int outOffset)
Computes the hash function on the in byte array and put the result in the output byte array |
javax.crypto.SecretKey |
generateKey(java.security.spec.AlgorithmParameterSpec keyParams)
Generates a secret key to initialize this UH object. |
javax.crypto.SecretKey |
generateKey(int keySize)
Generates a secret key to initialize this UH object. |
java.lang.String |
getAlgorithmName()
|
int |
getInputSize()
This function has multiple roles depending on the concrete hash function. |
int |
getOutputSize()
|
boolean |
isKeySet()
An object trying to use an instance of UH needs to check if it has already been initialized. |
void |
setKey(javax.crypto.SecretKey secretKey)
Sets the secret key for this UH. |
| Method Detail |
|---|
void setKey(javax.crypto.SecretKey secretKey) throws java.security.InvalidKeyException
secretKey
- secret key
java.security.InvalidKeyException
boolean isKeySet()
java.lang.String getAlgorithmName()
int getInputSize()
int getOutputSize()
javax.crypto.SecretKey generateKey(java.security.spec.AlgorithmParameterSpec keyParams) throws java.security.spec.InvalidParameterSpecException
keyParams
- algorithmParameterSpec contains the required parameters for the key generation
java.security.spec.InvalidParameterSpecException
javax.crypto.SecretKey generateKey(int keySize)
keySize
- is the required secret key size in bits
void compute(byte[] in, int inOffset, int inLen, byte[] out, int outOffset) throws javax.crypto.IllegalBlockSizeException
in
- - input byte arrayinOffset
- - the offset within the input byte arrayinLen
- - length. The number of bytes to take after the offsetout
- - output byte arrayoutOffset
- - the offset within the output byte array
javax.crypto.IllegalBlockSizeException
- if the input length is greater than the upper limit
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
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Terms & Conditions
You are required to read and understand the below information.
I declare that the information and details furnished in this application form are true and correct to the best of my knowledge and belief, and I will not willfully suppressed any material fact. I hereby declare that the information given above is true to the best of my knowledge, information and belief.
CCMA(“the Company”) may obtain a “consumer report” about you from a consumer reporting agency for employment purposes. A “consumer” report is a background screening report that may contain information regarding your criminal history, driving history, and other information about you. It may bear upon your character, general reputation, personal characteristics, and/or mode of living.


Miscellaneous Stuff
Check my new nome page - |rad.i.am|.
Alternatively, get thee to Funk Central (My old home page, my main man)
Or, visit my Geocities neighbours in the sunny Tropics, or my more immediate neighbours on this block
Aussie links
(A none too pretty catalogue of where I've been, where I may go, gutters slept in, etc.)
New South Wales
- I stayed here at Sydney Central on the first few nights. A bit like a hotel, really, and you didn't get to meet many people. Talk to Nadine behind the desk if you go, though, as she's cool as.
- A very fine place to chill out, Byron Bay. Much new age business going on.
- Finally I got it uploaded - my first attempt at professional web design, for Js Bay YHA Hostel.
- A collection of Australia's BIG THINGS, including a shot of Nimbin, courtesy of the High-Tech Tourist.
Queensland
And that's about all for the links as I've put the best ones into the diary entries themselves.
Back to the main page


Copyright (c) 2010-2011, The Dojo Foundation
Copyright (c) 2010-2012 Google, Inc. http://angularjs.org
© 2009–2013 by Jeff Mott. All rights reserved.
Copyright (c) 2012 Mark Hayes
Copyright 2005, 2013 jQuery Foundation, Inc. and other contributors
Copyright (C) <year> <copyright holders>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright (c) <year>, <copyright holder>
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.


Also known as "{{ object.common_name }}".
Active from {{ object.active_period_text }}.
For election IDs, this should be referred to as {{ object.slug }}
The official identifier is {{ object.official_identifier }}
The organisation type is {{ object.organisation_type }}
This organisation supports the following election types:
{{ election_type.election_type }}


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A = Number of patient reports in which the patient took the drug and experienced the reaction
B = Number of patient reports in which the patient took the drug, but did not experience the reaction
C = Number of patient reports in which the patient did not take the drug, but did experience the reaction
D = Number of patient reports in which the patient did not take the drug and did not experience the reaction
T = Total number of patient reports
The proportional reporting ratio (PRR) is a statistic that is used to summarize the extent to which a particular adverse
event is reported for individuals taking a specific drug, compared to the frequency at which the same adverse event is reported for patients
taking some other drug (or who are taking any drug in a specified class of drugs). 1
A PRR greater than 1 suggests that the adverse event is more commonly reported for individuals taking the drug of interest, relative
to the comparison drugs. This could indicate that the adverse event is caused by the drug of interest and therefore a "side effect",
although a PRR exceeding 1 could also reflect sampling variation in the data, reporting errors, biased reporting, multiple reports of the
same case or the same patient, or a number of other causes.1
1 - "Proportional reporting ratio." Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 22 July 2004. Web. 23 June. 2015


Enter URL starting with www
Renewable energy is useful energy that is collected from renewable resources, which are naturally replenished on a human timescale, including carbon neutral sources like sunlight, wind, rain, tides, waves, and geothermal heat. The term often also encompasses biomass as well, whose carbon neutral status is under debate. This type of energy source stands in contrast to fossil fuels, which are being used far more quickly than they are being replenished.
Web hosting providers consume a great amount of energy to power servers and data centre for providing 24/7 services. Other related facilities such as cooling controls and enhanced security setups also require a lot of electricity. Such a great amount of energy usage has a negative impact on the environment. Green web hosting, as the name implied, is the web hosting solution powered by environmental friendly resources such as renewable energy.
Renewable energy is generated through natural resources. It is naturally 're-usable'. Hydroelectricity and wind power are often the most common natural resources. In North America, electricity is mostly generated by renewable energy. For example, according to Wikipedia, "Canada is the world's second largest producer of hydroelectricity in the world and one of few countries to generate the majority of its electricity from hydroelectricity (59% in 2006)". In daily operations, a handful web hosting firms start taking proactive solutions such as using more efficient server hardware to reduce the electricity usage. All of these practices become very common in North American web hosting industry.


---
layout: recipe
title: "Brownie"
tags: desert chocolate
author: Gogo
preview_picture: "/assets/images/brownie.jpg"
ingredients:
servings: 8
list:
- name: butter
quantity: 230
unit: g
- name: dark chocolate
quantity: 200
unit: g
- name: sugar
quantity: 200
unit: g
- name: eggs
quantity: 4
- name: flour
quantity: 100
unit: g
- name: "nut: walnut, hazelnut or almond"
quantity: 130
unit: g
excerpt_separator:
---


BARB WIRE 1996 A film review by Timothy Voon Copyright 1997 Timothy Voon
Cast: Pamela Anderson Lee, Temura Morrison, Victoria Rowell, Jack Noseworthy, Xander Berkeley, Udo Kier Director: David Hogan Screenplay: Chuck Pfarrer and Ilene Chaiken
She leaves a trail of dead men behind her. Hates to be called ‘babe', which results in violent mood swings, more often than not resulting in the male offender's untimely death. She struts, and stalks her prey with the style of a night club stripper, and calls herself ‘Barb Wire' to give her gimmick an edge.
The year is 2020 and we are in the midst of the second American Civil War (Is there anything left to fight about?) A new breed of hero has evolved - an ex-marine, very pre-pre-pre menstrual, angry woman, dumped by her boyfriend, who has a lot on her mind, and a lot to prove to the world (Hey, I thought that was G.I. Joe's set-up?).
What Ms. Barb Wire (Anderson Lee) needs to understand is that less flesh, means more mystery; silicon belongs in computer chips, and not part of the mammary glands; too much leather constitutes a sofa, not clothing; a name like ‘Barb Wire' rusts easily, and belongs on the metal scrap pile; and no, there won't be a long line of future sequels.
This ‘babe', (ooops, please don't shoot me) is history.
Comment: Bring back Barbarella.
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Course Syllabus
Week 01 - Introduction
Week 02 - Functions
Week 03 - Algorithms
Week 04 - Coupling
Week 05 - Patterns
Week 08 - Objects
Week 09 - Transformations
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Module mice
Library for accesing a mouse.
This library allows to read data from a mouse,
such as it's coordinates and button presses.
The device will be named "mice", module "mice".
| events |
Events emitted by this device. |
| conf |
Configuration Table. |
| name |
Name of the device (in this case, 'mice'). |
| module |
Module name (in this case, 'mice'). |
| filename |
Device file of the mouse. |
Functions
-
init (conf)
-
Initialize and starts the module.
This is called automatically by toribio if the load attribute for the module in the configuration file is set to
true.
Parameters:
- conf
the configuration table (see conf).
-
get_pos ()
-
Get mouse position.
Returns:
a pair of x, y coordinates.
-
reset_pos (newx, newy)
-
Reset position.
Fixes the coordinates associated to the current
position.
Parameters:
- newx
number to set as x coordinate of
the cursos (defaults to 0)
- newy
number to set as y coordinate of
the cursos (defaults to 0)
-
set_pause (pause)
-
Pause the event generation.
While the device is paused, no events are generated, nor movements tracked.
Parameters:
- pause
mode, true to pause, false to unpause
Tables
-
events
-
Events emitted by this device.
Button presses have single parameter: true on press,
false on release.
Fields:
- leftbutton
Left button click.
- rightbutton
Right button click.
- middlebutton
Middle button click.
- move
Mouse moved, first parameter x, second parameter y coordinates.
-
conf
-
Configuration Table.
When the start is done automatically (trough configuration),
this table points to the modules section in the global configuration table loaded from the configuration file.
Fields:
- load
whether toribio should start this module automatically at startup.
- filename
the device file for themouse (defaults to ''/dev/input/mice'').
Fields
-
name
-
Name of the device (in this case, 'mice').
-
module
-
Module name (in this case, 'mice').
-
filename
-
Device file of the mouse.
For example, '/dev/input/mice'


--- layout: post title: Hello twitter? date: '2008-06-11T10:22:00.001+01:00' tags: modified_time: '2008-06-11T10:22:27.395+01:00' blogger_id: tag:blogger.com,1999:blog-4015568221071268916.post-3769038787296266230 comments: true blogger_orig_url: http://serialseb.blogspot.com/2008/06/hello-twitter.html ---
Craig has been selling me twitter over the last few months and I always kept it on the back of my mind. From this morning twitteroo is installed and I'll be able to start moaning on my twitter feed about the poor QoS as soon as I get it. Right now it just looks like a web-based IRC to me.
Side note, when is too much information too much? Mailing lists, messenger (and often during the day visitors of this blog chatting away, usually introducing themselves with asdf or the time-proven testing), maybe I'm overcommitting?


Scuba diving is a mode of underwater diving where the diver uses a self-contained underwater breathing apparatus (scuba), which is completely independent of surface supply, to breathe underwater.
The cancellation is free of charge 2 days prior to the
date of arrival,
This is a sample description
The cancellation is free of charge 2 days prior to the
date of arrival,
This is a sample description
The cancellation is free of charge 2 days prior to the
date of arrival,
This is a sample description
The cancellation is free of charge 2 days prior to the
date of arrival,
This is a sample description
A bunch of text


{% extends "base.html" %} {% load cg_template_filters %} {% block head %} {##} {% endblock %} {% block container-content %}
To begin running an application, select it in the list below and then click the 'Launch' button in the image details section of the application info.
| Application Name |
Description | Keywords | Branch | Category | Subcategory | Owner |
|---|


link name
is a link to another nifty site
This is a Title
[[ AfterTitleArea ]]
Embed content where you wish
| Regular HTML | HTML from Article |
|
Writing and testing
|
[[ TableArea ]]
|
send me mail at
<EMAIL_ADDRESS>this is a new paragraph!
this is a new paragraph!
this is a new sentence without a paragraph break, in bold italics.
[[ EndPageArea ]]