audiosegment module¶
This module simply exposes a wrapper of a pydub.AudioSegment object.
-
class
audiosegment.
AudioSegment
(pydubseg, name)[source]¶ Bases:
object
This class is a wrapper for a pydub.AudioSegment that provides additional methods.
-
auditory_scene_analysis
(debug=False, debugplot=False)[source]¶ Algorithm based on paper: Auditory Segmentation Based on Onset and Offset Analysis, by Hu and Wang, 2007.
Returns a list of AudioSegments, each of which is all the sound during this AudioSegment’s duration from a particular source. That is, if there are several overlapping sounds in this AudioSegment, this method will return one AudioSegment object for each of those sounds. At least, that’s the idea.
Current version is very much in alpha, and while it shows promise, will require quite a bit more tuning before it can really claim to work.
- Parameters
debug – If True will print out debug outputs along the way. Useful if you want to see why it is taking so long.
debugplot – If True will use Matplotlib to plot the resulting spectrogram masks in Mel frequency scale.
- Returns
List of AudioSegment objects, each of which is from a particular sound source.
-
detect_event
(model, ms_per_input, transition_matrix, model_stats, event_length_s, start_as_yes=False, prob_raw_yes=0.5)[source]¶ A list of tuples of the form [(‘n’, AudioSegment), (‘y’, AudioSegment), etc.] is returned, where tuples of the form (‘n’, AudioSegment) are the segments of sound where the event was not detected, while (‘y’, AudioSegment) tuples were the segments of sound where the event was detected.
# Example usage import audiosegment import keras import keras.models import numpy as np import sys class Model: def __init__(self, modelpath): self.model = keras.models.load_model(modelpath) def predict(self, seg): _bins, fft_vals = seg.fft() fft_vals = np.abs(fft_vals) / len(fft_vals) predicted_np_form = self.model.predict(np.array([fft_vals]), batch_size=1) prediction_as_int = int(round(predicted_np_form[0][0])) return prediction_as_int modelpath = sys.argv[1] wavpath = sys.argv[2] model = Model(modelpath) seg = audiosegment.from_file(wavpath).resample(sample_rate_Hz=32000, sample_width=2, channels=1) pyes_to_no = 0.3 # The probability of one 30 ms sample being an event, and the next one not pno_to_yes = 0.2 # The probability of one 30 ms sample not being an event, and the next one yes ptrue_pos_rate = 0.8 # The true positive rate (probability of a predicted yes being right) pfalse_neg_rate = 0.3 # The false negative rate (probability of a predicted no being wrong) raw_prob = 0.7 # The raw probability of seeing the event in any random 30 ms slice of this file events = seg.detect_event(model, ms_per_input=30, transition_matrix=[pyes_to_no, pno_to_yes], model_stats=[ptrue_pos_rate, pfalse_neg_rate], event_length_s=0.25, prob_raw_yes=raw_prob) nos = [event[1] for event in events if event[0] == 'n'] yeses = [event[1] for event in events if event[0] == 'y'] if len(nos) > 1: notdetected = nos[0].reduce(nos[1:]) notdetected.export("notdetected.wav", format="WAV") if len(yeses) > 1: detected = yeses[0].reduce(yeses[1:]) detected.export("detected.wav", format="WAV")
- Parameters
model – The model. The model must have a predict() function which takes an AudioSegment of ms_per_input number of ms and which outputs 1 if the audio event is detected in that input, and 0 if not. Make sure to resample the AudioSegment to the right values before calling this function on it.
ms_per_input – The number of ms of AudioSegment to be fed into the model at a time. If this does not come out even, the last AudioSegment will be zero-padded.
transition_matrix – An iterable of the form: [p(yes->no), p(no->yes)]. That is, the probability of moving from a ‘yes’ state to a ‘no’ state and the probability of vice versa.
model_stats – An iterable of the form: [p(reality=1|output=1), p(reality=1|output=0)]. That is, the probability of the ground truth really being a 1, given that the model output a 1, and the probability of the ground truth being a 1, given that the model output a 0.
event_length_s – The typical duration of the event you are looking for in seconds (can be a float).
start_as_yes – If True, the first ms_per_input will be in the ‘y’ category. Otherwise it will be in the ‘n’ category.
prob_raw_yes – The raw probability of finding the event in any given ms_per_input vector.
- Returns
A list of tuples of the form [(‘n’, AudioSegment), (‘y’, AudioSegment), etc.], where over the course of the list, the AudioSegment in tuple 3 picks up where the one in tuple 2 left off.
- Raises
ValueError if ms_per_input is negative or larger than the number of ms in this AudioSegment; if transition_matrix or model_stats do not have a __len__ attribute or are not length 2; if the values in transition_matrix or model_stats are not in the closed interval [0.0, 1.0].
-
detect_voice
(prob_detect_voice=0.5)[source]¶ Returns self as a list of tuples: [(‘v’, voiced segment), (‘u’, unvoiced segment), (etc.)]
The overall order of the AudioSegment is preserved.
- Parameters
prob_detect_voice – The raw probability that any random 20ms window of the audio file contains voice.
- Returns
The described list.
-
dice
(seconds, zero_pad=False)[source]¶ Cuts the AudioSegment into seconds segments (at most). So for example, if seconds=10, this will return a list of AudioSegments, in order, where each one is at most 10 seconds long. If zero_pad is True, the last item AudioSegment object will be zero padded to result in seconds seconds.
- Parameters
seconds – The length of each segment in seconds. Can be either a float/int, in which case self.duration_seconds / seconds are made, each of seconds length, or a list-like can be given, in which case the given list must sum to self.duration_seconds and each segment is specified by the list - e.g. the 9th AudioSegment in the returned list will be seconds[8] seconds long.
zero_pad – Whether to zero_pad the final segment if necessary. Ignored if seconds is a list-like.
- Returns
A list of AudioSegments, each of which is the appropriate number of seconds long.
- Raises
ValueError if a list-like is given for seconds and the list’s durations do not sum to self.duration_seconds.
-
fft
(start_s=None, duration_s=None, start_sample=None, num_samples=None, zero_pad=False)[source]¶ Transforms the indicated slice of the AudioSegment into the frequency domain and returns the bins and the values.
If neither start_s or start_sample is specified, the first sample of the slice will be the first sample of the AudioSegment.
If neither duration_s or num_samples is specified, the slice will be from the specified start to the end of the segment.
# Example for plotting the FFT using this function import matplotlib.pyplot as plt import numpy as np seg = audiosegment.from_file("furelise.wav") # Just take the first 3 seconds hist_bins, hist_vals = seg[1:3000].fft() hist_vals_real_normed = np.abs(hist_vals) / len(hist_vals) plt.plot(hist_bins / 1000, hist_vals_real_normed) plt.xlabel("kHz") plt.ylabel("dB") plt.show()
- Parameters
start_s – The start time in seconds. If this is specified, you cannot specify start_sample.
duration_s – The duration of the slice in seconds. If this is specified, you cannot specify num_samples.
start_sample – The zero-based index of the first sample to include in the slice. If this is specified, you cannot specify start_s.
num_samples – The number of samples to include in the slice. If this is specified, you cannot specify duration_s.
zero_pad – If True and the combination of start and duration result in running off the end of the AudioSegment, the end is zero padded to prevent this.
- Returns
np.ndarray of frequencies in Hz, np.ndarray of amount of each frequency
- Raises
ValueError If start_s and start_sample are both specified and/or if both duration_s and num_samples are specified.
-
filter_bank
(lower_bound_hz=50, upper_bound_hz=8000.0, nfilters=128, mode='mel')[source]¶ Returns a numpy array of shape (nfilters, nsamples), where each row of data is the result of bandpass filtering the audiosegment around a particular frequency. The frequencies are spaced from lower_bound_hz to upper_bound_hz and are returned with the np array. The particular spacing of the frequencies depends on mode, which can be either: ‘linear’, ‘mel’, or ‘log’.
Note
This method is an approximation of a gammatone filterbank until I get around to writing an actual gammatone filterbank function.
# Example usage import audiosegment import matplotlib.pyplot as plt import numpy as np def visualize(spect, frequencies, title=""): # Visualize the result of calling seg.filter_bank() for any number of filters i = 0 for freq, (index, row) in zip(frequencies[::-1], enumerate(spect[::-1, :])): plt.subplot(spect.shape[0], 1, index + 1) if i == 0: plt.title(title) i += 1 plt.ylabel("{0:.0f}".format(freq)) plt.plot(row) plt.show() seg = audiosegment.from_file("some_audio.wav").resample(sample_rate_Hz=24000, sample_width=2, channels=1) spec, frequencies = seg.filter_bank(nfilters=5) visualize(spec, frequencies)
- Parameters
lower_bound_hz – The lower bound of the frequencies to use in the bandpass filters.
upper_bound_hz – The upper bound of the frequencies to use in the bandpass filters.
nfilters – The number of filters to apply. This will determine which frequencies are used as well, as they are interpolated between lower_bound_hz and upper_bound_hz based on mode.
mode – The way the frequencies are spaced. Options are: linear, in which case the frequencies are linearly interpolated between lower_bound_hz and upper_bound_hz, mel, in which case the mel frequencies are used, or log, in which case they are log-10 spaced.
- Returns
A numpy array of the form (nfilters, nsamples), where each row is the audiosegment, bandpass-filtered around a particular frequency, and the list of frequencies. I.e., returns (spec, freqs).
-
filter_silence
(duration_s=1, threshold_percentage=1, console_output=False)[source]¶ Returns a copy of this AudioSegment, but whose silence has been removed.
Note
This method requires that you have the program ‘sox’ installed.
Warning
This method uses the program ‘sox’ to perform the task. While this is very fast for a single function call, the IO may add up for large numbers of AudioSegment objects.
- Parameters
duration_s – The number of seconds of “silence” that must be present in a row to be stripped.
threshold_percentage – Silence is defined as any samples whose absolute value is below threshold_percentage * max(abs(samples in this segment)).
console_output – If True, will pipe all sox output to the console.
- Returns
A copy of this AudioSegment, but whose silence has been removed. Note that if the arguments to this method result in it removing all samples from the audio, we issue a warning and return a copy of the original, unchanged audio.
-
generate_frames
(frame_duration_ms, zero_pad=True)[source]¶ Yields self’s data in chunks of frame_duration_ms.
This function adapted from pywebrtc’s example [https://github.com/wiseman/py-webrtcvad/blob/master/example.py].
- Parameters
frame_duration_ms – The length of each frame in ms.
zero_pad – Whether or not to zero pad the end of the AudioSegment object to get all the audio data out as frames. If not, there may be a part at the end of the Segment that is cut off (the part will be <= frame_duration_ms in length).
- Returns
A Frame object with properties ‘bytes (the data)’, ‘timestamp (start time)’, and ‘duration’.
-
generate_frames_as_segments
(frame_duration_ms, zero_pad=True)[source]¶ Does the same thing as generate_frames, but yields tuples of (AudioSegment, timestamp) instead of Frames.
-
human_audible
()[source]¶ Returns the number of seconds of human audible audio in this wave form. Note that I define whether a segment of the wave form is human audible based on doing an FFT of it and then checking for any peaks within 20 Hz to 20 kHz, a fair rule of thumb for human hearing thresholds.
Also note that I make no assumptions about the SPL of the sound. This is important because humans can only hear something if it is loud enough (obviously), but how loud something needs to be to be heard depends on its frequency. I do not model this with this method. If you are curious how the SPL threshold changes with frequency, take a look at the audiogram in Hearing Thresholds by Yost and Killion, 1997 (see https://www.etymotic.com/media/publications/erl-0096-1997.pdf).
- Returns
A floating point value representing the number of seconds (with 100 ms resolution).
-
reduce
(others)[source]¶ Reduces others into this one by concatenating all the others onto this one and returning the result. Does not modify self, instead, makes a copy and returns that.
- Parameters
others – The other AudioSegment objects to append to this one.
- Returns
The concatenated result.
-
resample
(sample_rate_Hz=None, sample_width=None, channels=None)[source]¶ Returns a new AudioSegment whose data is the same as this one, but which has been resampled to the specified characteristics. Any parameter left None will be unchanged.
This is mostly just a wrapper for calling pydub.AudioSegment’s set_sample_width, set_channels, and set_frame_rate methods, but allows for more channels than the 1 or 2 that Pydub allows.
- Parameters
sample_rate_Hz – The new sample rate in Hz.
sample_width – The new sample width in bytes, so sample_width=2 would correspond to 16 bit (2 byte) width. Note that 3-byte audio will be converted to 4-byte instead by Pydub.
channels – The new number of channels.
- Returns
The newly sampled AudioSegment.
-
spectrogram
(start_s=None, duration_s=None, start_sample=None, num_samples=None, window_length_s=None, window_length_samples=None, overlap=0.5, window=('tukey', 0.25))[source]¶ Does a series of FFTs from start_s or start_sample for duration_s or num_samples. Effectively, transforms a slice of the AudioSegment into the frequency domain across different time bins.
# Example for plotting a spectrogram using this function import audiosegment import matplotlib.pyplot as plt #... seg = audiosegment.from_file("somebodytalking.wav") freqs, times, amplitudes = seg.spectrogram(window_length_s=0.03, overlap=0.5) amplitudes = 10 * np.log10(amplitudes + 1e-9) # Plot plt.pcolormesh(times, freqs, amplitudes) plt.xlabel("Time in Seconds") plt.ylabel("Frequency in Hz") plt.show()
- Parameters
start_s – The start time. Starts at the beginning if neither this nor start_sample is specified.
duration_s – The duration of the spectrogram in seconds. Goes to the end if neither this nor num_samples is specified.
start_sample – The index of the first sample to use. Starts at the beginning if neither this nor start_s is specified.
num_samples – The number of samples in the spectrogram. Goes to the end if neither this nor duration_s is specified.
window_length_s – The length of each FFT in seconds. If the total number of samples in the spectrogram is not a multiple of the window length in samples, the last window will be zero-padded.
window_length_samples – The length of each FFT in number of samples. If the total number of samples in the spectrogram is not a multiple of the window length in samples, the last window will be zero-padded.
overlap – The fraction of each window to overlap.
window – See Scipy’s spectrogram-function. This parameter is passed as-is directly into the Scipy spectrogram function. It’s documentation is reproduced here: Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values, which are DFT-even by default. See get_window for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be window_length_samples. Defaults to a Tukey window with shape parameter of 0.25.
- Returns
Three np.ndarrays: The frequency values in Hz (the y-axis in a spectrogram), the time values starting at start time and then increasing by duration_s each step (the x-axis in a spectrogram), and the dB of each time/frequency bin as a 2D array of shape [len(frequency values), len(duration)].
- Raises
ValueError – If start_s and start_sample are both specified, if duration_s and num_samples are both specified, if the first window’s duration plus start time lead to running off the end of the AudioSegment, or if window_length_s and window_length_samples are either both specified or if they are both not specified.
-
property
spl
¶ Sound Pressure Level - defined as 20 * log10(p/p0), where p is the RMS of the sound wave in Pascals and p0 is 20 micro Pascals.
Since we would need to know calibration information about the microphone used to record the sound in order to transform the PCM values of this audiosegment into Pascals, we can’t really give an accurate SPL measurement.
However, we can give a reasonable guess that can certainly be used to compare two sounds taken from the same microphone set up.
Be wary about using this to compare sounds taken under different recording conditions however, except as a simple approximation.
Returns a scalar float representing the dB SPL of this audiosegment.
-
to_numpy_array
()[source]¶ Returns a numpy array. The shape of this array is either (nsamples, nchannels), if nchannels is greater than 1, or else just (nsamples,).
-
zero_extend
(duration_s=None, num_samples=None)[source]¶ Adds a number of zeros (digital silence) to the AudioSegment (returning a new one).
- Parameters
duration_s – The number of seconds of zeros to add. If this is specified, num_samples must be None.
num_samples – The number of zeros to add. If this is specified, duration_s must be None.
- Returns
A new AudioSegment object that has been zero extended.
- Raises
ValueError if duration_s and num_samples are both specified.
-
-
audiosegment.
deserialize
(bstr)[source]¶ Attempts to deserialize a bytestring into an audiosegment.
- Parameters
bstr – The bytestring serialized via an audiosegment’s serialize() method.
- Returns
An AudioSegment object deserialized from bstr.
-
audiosegment.
empty
()[source]¶ Creates a zero-duration AudioSegment object.
- Returns
An empty AudioSegment object.
-
audiosegment.
from_file
(path)[source]¶ Returns an AudioSegment object from the given file based on its file extension. If the extension is wrong, this will throw some sort of error.
- Parameters
path – The path to the file, including the file extension.
- Returns
An AudioSegment instance from the file.
-
audiosegment.
from_mono_audiosegments
(*args)[source]¶ Creates a multi-channel AudioSegment out of multiple mono AudioSegments (two or more). Each mono AudioSegment passed in should be exactly the same number of samples.
- Returns
An AudioSegment of multiple channels formed from the given mono AudioSegments.
-
audiosegment.
from_numpy_array
(nparr, framerate)[source]¶ Returns an AudioSegment created from the given numpy array.
The numpy array must have shape = (num_samples, num_channels).
- Parameters
nparr – The numpy array to create an AudioSegment from.
framerate – The sample rate (Hz) of the segment to generate.
- Returns
An AudioSegment created from the given array.
-
audiosegment.
silent
(duration=1000, frame_rate=11025)[source]¶ Creates an AudioSegment object of the specified duration/frame_rate filled with digital silence.
- Parameters
duration – The duration of the returned object in ms.
frame_rate – The samples per second of the returned object.
- Returns
AudioSegment object filled with pure digital silence.


SMELL OF CAMPHOR, FRAGRANCE OF JASMINE (Booye Kafoor, Atre Yas)
Reviewed by Harvey Karten New Yorker Films Director: Bahman Farmanara Writer: Bahman Farmanara Cast: Bahman Farmanara, Roya Nonahali, Reza Kianian, Valiyollah Shirandarni, Parivash Nazariye, Firouz Behjat, Mohammadi, Hossein Kasbian
Young people believe that they're immortal. They can barely think of tomorrow must less the idea of their own death. Since sixty-five percent of the citizens of Iran--the land which forms the backdrop of Bahman Farmanara's first film in 22 years--are under the age of twenty-five, not too many Farsi-speaking folks are much concerned about their mortality. But Mr. Farmanara--who has not only scripted and directed "Smell of Camphor, Fragrance of Jasmine" but appears in virtually every scene of this highly personal work-- certainly is...boy is he ever. That's about all he thinks about, dreams of, wonders about. Why? He's only fifty-five years old, but his health is rotten. He has had a couple of heart attacks, he has a pot belly, he smokes like a fiend, and he tosses out his physician's health plan as soon as he exits the man's office.
So aside from the fact that "Smell of Camphor" is about the writer-director's thoughts of his own demise, what recommends this film to us? Not really a heck of a lot, because Bahman Farjami, as Farmanara's alter ego is called in the story, acts out a plot that is second-string Ingmar Bergman. Where the great Swedish director in "The Seventh Seal" deals allegorically and agonizingly with the metaphysics of man's relationship to God and his encounters with the idea of death, the Iranian director--out of shape presumably because he has been away from the camera for two decades--deals in a fairly shallow manner with his anticipated parting from the planet. Where in "Wild Strawberries" Bergman deals with the subject of man's isolation and uses a journey as the plot structure, Farmanara's trip by train across the Iranian countryside pops up suddenly, now and then, as though the film's editor were having some fun with the splicing machine. As for isolation, true enough, Bahman Farjami has lost his wife some years back and is still in mourning, but his loneliness is not of the existential sort favored by Bergman in the far more resonant film.
Perhaps we're being unfair to compare the two: after all nowhere does Farmanara state that he has been influenced by the Swede. Taken on its own, however, "Smell of Camphor" is so talky, so lacking in humor (save for a single gag made by a white-haired funeral director with a neatly trimmed beard) that the gloom-and-doom inclination of the writer serves little transcendent effect.
Death and its foreboding are everywhere. Farjami's mother sits in a wheelchair staring into space, a tear flowing lightly down her right eye, as this victim of Alzheimer's is read a story by her son--whom she does not recognize or react to. Farjami's visits to his wife's grave upsets him, as he discovers that someone is already buried next to the woman in the plot which Farjami purchased for himself. Searching for a lost son-in-law, Farjami questions the mortician in a hospital who informs him that he has only two corpses on ice, one being that of a 15-year-old girl who has committed suicide. "Only the young have the courage to kill themselves: the old hold on to life with both arms," informs an associate, who does not elaborate.
The film is divided self-consciously into three acts as though this were a photographed play, but a drama in which too much of the action is of the inner variety--a meditation by a morose fellow who cracks a smile (without showing his teeth) once during the entire 93-minute drama. From time to time the editor slips in a serialized passage from a holy man who intones the way that the Koran specifies one should deal with a corpse. Early on, Farjami picks up a sullen woman who relates that she is running away from her abusive husband and who deposits an unusual gift for the driver on the back seat when she leaves. There is even a segment devoted to pointlessly abstract political talk by (I believe) Katami, the supposedly liberal president who was recently elected and who has just a modicum of power--but enough, presumably, to allow "Smell of Camphor" to pass censorship even though it does not deal with little children.
There is little substantive symbolism in this effort, though the camphor in the title refers to death and the jasmine alludes to youth--in whose custody lies hope for the future. The picture, which was picked up by the New York Film Festival, is in the Farsi language with English subtitles.
Not Rated. Running time: 93 minutes. (C) 2000 by Harvey Karten<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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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.
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Related links: index of all rec.arts.movies.reviews reviews


This little website, centrilizes talk given in the Rust Meetup Paris by
any Rustacean who want.
All of the presentation are available under Creative-Common parternite.
| Date | Titre | Organisateur | Lieu | Langue | Ressources |
|---|---|---|---|---|---|
| 2014 February | Felix S Klock II | Introduction to Rust | MozSpace of Paris | English | Slides |
| 2014 February | Nicolas Silva | My impression of Rust as a C++ programmer. | MozSpace of Paris | English | Slides |
| 2014 February | Axel Viala | Introduction to Rust | MozSpace of Paris | English | Slides |
Source on on github


Chronologically, we answer these questions:
1. Are there more people apply H1-B petition over years?2. Is it getting easier to get petition approved?3. Shall I apply early or late?
H-1B visa application declines for first time in 7 years last year!
Is foreign interest in American jobs wanning under Trump? Check out this article.
Click to H-1B Petition and Denied Rate from 2011-2017:
Petition Submission Distribution and Certification Rate:


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- Improved stability when visiting profiles on Mac and Ubuntu.
| |
| |
v4.3
- Support for licensing via affiliate network.
- Improved handling of unidentified users.
- Improved stability when auto-visiting.
- Improved throttling (Pro)
- Business Plus support
- Profile quickscan (Pro)
- Configurable throttling (Pro)
| |
| |
v4.2
- Use Google to search for profiles straight from the Dux-Soup menu
- Search on multiple tabs at the same time
- Visit members of a group via the Group page in LinkedIn
- Automatically skips profiles you visited in the last 7 days
- Thottle visit speed to reduce load on PC (Pro)
- Up to 400 visits per day via standard LinkedIn (Pro)
- Up to 1500 visits per day via Sales Navigator (Pro)
| |
If you enjoy Dux-Soup, why not drop us a line, or rate us in the Chrome webstore!
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Not Atomically Thinking™ | Discord Server Rules
⚠ WARNING: Server rules are expected to be updated in every revamp that the server goes through
which can be every two months. Please go to our Discord Page to view more information. Rules will be updated
on the website before the initial revamp (15 - 30 minutes before or even at such times like 1 day before initial rules are set).
But due to GitHub Pages Hosting Delay, it will take about 2 to 10 minutes for changes to be visible.
In the information below, you will find a Purpose section that explains why we need such rules, then below that is the
Preface section. In this section, you will find the introduction to the rules and a few things to keep in mind because they are common sense.
And then you have your Server Rules. If you have any questions about our server rules, please email us or text one of our server administrators/moderators for assistance.
Purpose
In Not Atomically Thinking, it is our mission to keep everyone in the server safe and welcoming for everyone.   In the Rules section, it will outline most of the "what to do and what not to do", in the Discord Server. We incorage that everyone reads the rules so that they can protect themselves and so they can know what is right, and what is wrong.
Preface
⚠ Remember: All rules in the server apply at all times and in all places. Most of the time, broken rules result in a warning - these expire with time
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trying to make people spam ping you through a nickname, or quoting/replying to rule-breaking messages. Basically, trying people to admit the wrong when they are
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We must also uphold Discord's Terms of Service and Community Guidelines
Server Rules
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• Begging to gain a role is not accepted. If you consecutively ask for the role, you might be kicked because you are wasting time of my staff and you are not being respectful.
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• If there is anybody disrespecting staff there shall be a PERMANENT BAN against the user.


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equals()
calls that compare two arrays.
Calling equals()
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if (first.equals(second)) return;
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After the quick-fix is applied:
void sample(int[] first, int[] second){
if (Arrays.equals(first, second)) return;
}


Design Philosphy¶
Before digging into the code, it’s recommended to better understand the philosophy behind engineering.
Everything we do should be in the name of software quality.
Everything.
Lean Principles¶
Lean principles guide everything. The tricky part is the fact that quality means different things to different people.
- External Customers - End Users: Your end users are the reason we all get paid. Listen to them.
- Internal Customers - Ops: Nowadays with Devops, some lines are blurred here, but when it comes to production outages, it’s rare a developer is called in to mess around with infrastructure. Partner with your infrastructure team and ensure the software behaves like they would expect it to without any ‘tribal knowledge’ or hacks.
- Internal Customers - Engineers: Your codebase should get you excited about writing code. If it doesn’t, have a retrospective with teammates and get on track. It should not be difficult to change (fragile), or prone to failure. What was your first day like as a new developer on a codebase? Was the product well documented, and the standards and expectations made clear? Technical debt is something that needs to be measured, managed, or at least observed and refactored continually.
If you’d like to read about the history and interactions between Lean Manufacturing and Software Design, I’d highly recommend Implementing Lean Software Development. Next, let’s cover some principles already established in the industry.
S.O.L.I.D. Principles¶
WIP


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Drone videography has become the hottest trend in event marketing, specifically, in recent times, providing a unique and engaging way to capture video footage - which can deliver significant benefits for your campaigns. And considering that video is the best performing content type across all social platforms, looking for new and engaging ways to incorporate video could be key - here's a look at some of the ways in which drone videos can help to drive more engagement for your digital marketing efforts.
The reason why photography is important is that it freezes memories. It captures a moment in time that you’ll be able to remember and cherish years from now.If you look at the images that people save, usually it’s photos of their family, friends, pets, places, and items they love. You take pictures of things that are significant to you.
The CRANE 3-AXIS features 360° pan, 320° tilt, and 360° roll adjustment. Multiple different special shooting modes allow you to get extra creative with the gimbal. Full-range POV mode enables 360° synchronous movement on all three axes. This means that the camera will lock into a single position and follow your movements without any limits, as opposed to evening out at some point.


 Moore gives an incredibly strong performance filled with dedication to her craft thanks largely in part to the guidance of experienced veteran director Ridley Scott. Scott, whose films include 'Alien', 'Blade Runner' and 'Thelma and Louise' has crafted a film with some of the most intense basic training ever seen in a motion picture. Stanley Kubrick's 'Full Metal Jacket' from 1987 featured an incredibly harsh treatment of raw recruits being inducted into the U.S. Marines but that film is tame in comparison to this.
Moore plays the first woman in the U.S. military to be trained as a Navy Seal. This reportedly is supposed to be the toughest training of any fighting force anywhere. Apparently, they have a drop out rate of 60 per cent. The master chief of the unit (Viggo Mortensen) vows that the first day will not end until someone drops out. The first day of training lasts 19 hours and the trainees look very physical in their roles. Attempts are made to give Moore preferential treatment since she is a woman. She resists any attempts at special treatment and tells her commanding officer that she wants to be treated EXACTLY the same way as every other man in her unit. Her three months of training hit some rocky spots from officials within the military who conspire against her in their attempt to keep the Navy Seal unit entirely dominated by men.
Ridley Scott's control of the camera in working with director of photography Hugh Johnson plunges the audience directly into the action with authentic scenes of military training both compelling and convincing. Demi Moore is thoroughly convincing in a very demanding physical role she plays with total credibility. She even sports a buzz cut to her hair to give the role more punch.
'G.I. Jane' is primarily a basic training film which is not a film classic but rather a slick opportunity to present a visual style of the military not done before in motion pictures. Anne Bancroft has some of the best work of her career in years as a U.S. Senator who recommends Moore as the first female candidate. She plays her role perfectly and convincingly by providing the basis of high rolling bureaucracy in doing what's convenient at the time of her profile among her peers and in front of the media complete with hypocrisy and different loyalties to suit the moment. 'G.I. Jane' is a film that rightfully knows the limits of its subject matter.
OUT OF 5> * * * *
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


Arena Manager:
This arena is open to all characters. Only the victors survive! Are you confident enough to accept the challenge?
Register team.
Cancel team registration.
[npc_%objectId%_viewTeams|View participating teams.]
View game rules.
View team with the most wins.
[npc_%objectId%_teleOut|Move to Fantasy Isle.]


T1 E PASS APPLICATION FORM
Applicant Personal Details
Name:
Father Name:
Spouse Name:
Gender :
Male
Female
Other
Age:
Phone Number:
Current Address:
No of Passengers:
SUBMIT


Male
Female
18 to 25 years old
26 to 29 years old
30 to 35 years old
36 than 40 years old
More than 40 years old
English
Chines
Korean
Japan
Khmer
Thai
At least 1 years
0$ - 199 USD
200 - 499 USD
1,000 - 1,999 USD
2,000 USD+
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Minima maxime quam architecto quo inventore harum ex magni, dicta impedit.
Bachelor's degree; at least 2 years of work experience that can be demonstrated to be applicable to the duties listed in the job description.
Being a Membership of the Expert Database page provides many advantages with the Promoting or Updating the self-skill developed on the global market.


{% extends "workflow/project_base.html" %} {% block title %}Workflows{% endblock title %} {% block nav_item_workflow %}active{% endblock nav_item_workflow %} {% block content %}
Something short and leading about the collection below—its contents, the creator, etc. Make it short and sweet, but not too short so folks don’t simply skip over it entirely.


Network monitoring in terms of routers if a link-down occurs on a router, there will be a notification from the dashboard of IPBB that the linkdown condition and notify the cause and other routers affected by the core router. Then, displaying the top 10 high utilization routers from all over Indonesia
In this Project the Main Objectives that needs by Telkomsel Directorate. Such as
Impact of the Dashboard to monitor utilization, link down, router performance and export data. This shows all backbone routers in all of Indonesia.


| major | minor | patch | ||
|---|---|---|---|---|
| {{currentVersion[0]}} | . | {{currentVersion[1]}} | . | {{currentVersion[2]}} |
If you have multiple changes for the same category — for instance, two data uploads— add them as separate release notes. They'll be combined before the upload.
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Event is based on technical and non-technical know how on a particular topic. They have to choose any one from given two topics written in chits on the spot and they need to speak for technically and non-technically for at least one minute on that topic. This will be elimination round.
Round 1: Describe topic technically and non-technically
In this round, the team of two students has to speak at least for one minute technically and one minute non-technically on chosen topic. Based on their speech they will be given points. After that they need to answer the questions asked by audience.
Audience is allowed to ask at max 3 questions. Extra points will be awarded to the team by giving right answer and if team will not be able to answer, or gives wrong answer then points will be deducted for that team.
Round 2 (Final Round): Speak on solved puzzle for at least 3 minutes
In this round team has to select one chit and they need to solve the puzzle in that chit after solving the puzzle they will get answer of scientist name and then they need to speak about that scientist for at least 3 minutes. They can speak about scientist’s life, achievements, given theories, proofs and whatever things they know about them. Winner team will be decided based on content of their speech.
Rules:
1. Each member has to speak at least one minute compulsory.
2. In round 1 - they will be given maximum 20 points based on their speech , and if they give correct answer on questions asked by audience then 3 points will be rewarded else 2 points will be deducted.
3. In round 2 - they need to speak for at least 3 minutes.
2
6th March 2017
Round 1 -9:00 to 10:00 AM Round 2- 11:00 to 12:00 PM
40/- per team
Parthit Sardhara<PHONE_NUMBER>)
Earnest Vakeriya<PHONE_NUMBER>)


River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods.<img src=“http://feeds.feedburner. …


Vancouver Island man found guilty in murder of wealthy California woman, 29 years later - Barriere Star Journal
by Robert BarronShawnigan Lake man killed wealthy senior in 1990
A Vancouver Island man has been found guilty in the murder of a California woman 29 years ago.
Anthony Michael Kubica, 63, was convicted by jury of first-degree murder on Nov. 19 for the killing of Marie Darling, 78, after just a single day of deliberations in Banning, California.
The jury found the Shawnigan Lake man guilty of kidnapping and murdering the wealthy Palm Springs resident who disappeared from her home in 1990.
RELATED STORY: Anthony Kubica to stand trial for murder in California
Darling’s body was found wrapped in a sleeping bag by hikers alongside a highway, with her feet bound in duct tape.
An autopsy performed on the body at the time found the cause of death to be blunt force trauma to the head.
Kubica’s sentencing is set for Jan. 10, but his lawyers have already filed a motion to dismiss the verdict.
John Hall, a public information officer with Riverside County’s district attorney’s office, said the prosecution was confident in the evidence in the case.
“We do not file charges in any case unless we believe we can prove to a jury that a defendant is guilty beyond a reasonable doubt,” he said.
“That is the standard we must abide by in any case.”
It was discovered after Darling’s body was found that more than $184,000 had been transferred out of her Swiss bank account to an account in Anguilla that Kubica, who lived and operated a company in Palm Springs at the time, had allegedly opened around the same time the body was found.
RELATED STORY: Judge says enough evidence for extradition in case of Shawnigan Lake man
Kubica and his wife, whom the court documents refer to as CJ, were suspects early in the investigation but the trail went cold.
A cold-case investigator later discovered that Kubica’s wife was Darling’s financial advisor, even though she’d denied knowing Darling during the original investigation.
Kubica’s wife has since died.
Kubica was living in the Cowichan Valley in 2017 when California issued a request for him to extradited to the U.S. to stand trial.
The B.C. Supreme Court ruled there was enough evidence for him to be extradited to face charges of murder and kidnapping last year. Kubica was extradited in late 2018, after final approval from former federal Justice Minister Jody Wilson-Raybould
RELATED STORY: Kubica extradited to U.S. on murder charges


[t]You have approved the reaction.[/t]
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Comparative Population Genetics Among Trematode Parasites of Salmonids in the Pacific Northwest
EPA Grant Number: U916130Title: Comparative Population Genetics Among Trematode Parasites of Salmonids in the Pacific Northwest
Investigators: Criscione, Charles D.
Institution: Oregon State University
EPA Project Officer: Graham, Karen
Project Period: January 1, 2003 through January 1, 2006
Project Amount: $108,344
RFA: STAR Graduate Fellowships (2003) Recipients Lists
Research Category: Fellowship - Zoology , Academic Fellowships , Biology/Life Sciences
Objective:
The overall objective of this research project is to compare the population genetic structure of four species of parasitic trematodes that infect salmon. Two species complete their life cycles entirely within a freshwater habitat (autogenic life cycle). Two species cycle through birds and mammals and have a greater opportunity to disperse between aquatic habitats (allogenic life cycle). The specific objective of this research project is to use mitochondrial and Internal Transcribed Spacer DNA sequence data to test if there is more gene flow and less population structure among populations of the allogenic species than of the autogenic species.
Approach:
Population genetic structure (PGS) refers to the distribution of genetic variation within and among populations of a species. Genetic structure is largely controlled by the effective sizes of the populations and rates of gene flow (dispersal) among them. Consequently, an understanding of parasite PGS provides insight into evolutionary processes such as speciation, extinction, and adaptation to host defenses. From an environmental perspective, PGS is important for the dynamics of disease transmission and adaptive potential for drug resistance (Anderson and May, 1991). Nevertheless, there have been few studies on PGS in parasitic helminths (cestodes, trematodes, nematodes), and studies on the factors that influence PGS of helminth parasites are virtually nonexistent. This lack of data is surprising given the medical, veterinary, and economic (e.g., fisheries) importance of helminths (Roberts and Janovy, 2000).
My primary goal is to examine the factors that control parasite dispersal among populations. For parasitic helminths, life history characteristics of both the parasites and their hosts will influence dispersal capabilities. An ecological hypothesis put forth by Esch, et al. (1988) provides an integrated framework in which to test factors that affect parasite gene flow. In examining helminth community structure in freshwater fish, Esch, et al. (1988) distinguished between two modes of parasitic transmission: autogenic and allogenic. Autogenic parasites mature in fish and complete their life cycle entirely within an aquatic habitat. Allogenic parasites cycle through the freshwater environment, but mature in terrestrial hosts such as birds and mammals. The land and sea are ecological barriers that limit the dispersal of freshwater-dependent hosts utilized by autogenic parasites; therefore, allogenic species should have a greater dispersal potential because their terrestrial hosts may circumvent these barriers (Esch, et al., 1988). The same conceptual basis can be used to examine the effects of host movement on PGS within parasite species. Thus, the population genetics questions I will test are as follows:
1. Because of the vagility of their terrestrial hosts, do allogenic parasites have less subdivided populations (i.e., more gene flow) than autogenic parasites? I will use molecular markers from digenetic trematodes that parasitize salmonid fishes in the Pacific Northwest to test the autogenic/allogenic hypothesis. The proposed study organisms include two autogenic trematodes, Plagioporus shawi and Deropegus aspina, and two allogenic species, Nanophyetus salmincola and Apophallus donicus.
2. Are there cryptic species among the populations of proposed study organisms? Populations with restricted gene flow are most likely to be comprised of cryptic species (genetically distinct, but morphologically indistinguishable). A corollary prediction is that cryptic species are more likely to be found among parasites with autogenic life cycles.
Supplemental Keywords:
fellowship, population genetics, population genetic structure, PGS, trematode parasites, helminth parasites, parasites, salmonids, Pacific Northwest, salmon, freshwater habitat, aquatic habitat, allogenic species, autogenic species, genetic variation, nematodes, trematodes, cestodes.Relevant Websites:
2004 STAR Graduate Fellowship Conference Poster (PDF, 1p., 164KB, about PDF)


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status
Return an unspecified value.
Core Module Index | Main Index


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- or -
Get your key and use it anywhere in your HTTP call:
In your path:
http:///testmycall/newdata/<key>
In your HTTP headers:
X-Debug-My-HTTP: <key>
Put it in the HTTP body with:
$ curl -X POST -d '<key>' http:///testmycall/
- finally -


THE COWBOY WAY A film review by Michael John Legeros Copyright 1994 Michael John Legeros
Directed by Gregg Champion Written by Bill Wittliff Cast Woody Harrelson Kiefer Sutherland MPAA Rating "PG-13" (but a *strong* PG-13..) Running Time Approx. 100 minutes
==
"I'm a bid-ness man." - Harrelson to Sutherland
Woody Harrelson *is* THE COWBOY WAY.
The former bartender of TV's "Cheers" is the *sole* reason to watch this clunky action/comedy about a pair of rodeo-riders roaming the Big Apple to find their missing friend. When they discover that said friend is dead, they take their own approach to solving the crime.
COOGAN'S BLUFF it ain't, but this clunky caper comedy has about forty-minutes of wit worth watching.  The price of admission also buys the viewer a chance to see (a) Woody with a crew cut and (b) Woody wearing nothing but his hat.
How's that for a decent proposal?
The rest of the rest is a lame-brain sweat-shop subplot that stops making sense after about an hour. The finale is an absurd horse/subway chase that's calmed by the sight of Harrelson and Sutherland riding horseback through The City. Remember when they still made movies there?
Oddly, THE COWBOY WAY has much more cruelty than the rating (of PG-13) suggests. The demise of the villain, in particular, is quite disturbing and parents should be wary.
Grade: C+
.
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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Address masquerading is a method to hide all hosts below a domain behind their
mail gateway, and to make it appear as if the mail comes from the gateway
itself, instead of from individual machines.
Address masquerading is disabled by default. To enable, specify one or more
domain names separated by whitespace or commas. For example: $mydomain
; in this example, addresses of the form<EMAIL_ADDRESS>would be
rewritten to user@$mydomain.


You imbue plants within 30 feet of you with limited sentience and animation, giving them the ability to communicate with you and follow your simple commands. You can question plants about events in the spell's area within the past day, gaining information about creatures that have passed, weather, and other circumstances.
You can also turn difficult terrain caused by plant growth (such as thickets and undergrowth) into ordinary terrain that lasts for the duration. Or you can turn ordinary terrain where plants are present into difficult terrain that lasts for the duration, causing v ines and branches to hinder pursuers, for example.
Plants might be able to perform other tasks on your behalf, at the DM's discretion. The spell doesn't enable plants to uproot themselves and move about, but they can freely move branches, tendrils, and stalks.
If a plant creature is in the area, you can communicate with it as if you shared a common language, but you gain no magical ability to influence it.
This spell can cause the plants created by the entangle spell to release a restrained creature.