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Note: If you see a "403 - Forbidden" error above, you still need to click "I understand and accept" on the competition rules page.
Three files are downloaded:
train.csv: training data (contains features and targets)
test.csv: feature data used to make predictions to send to Kaggle
gender_submission.csv: an example com... | # Your code goes here | content/04_classification/04_classification_project/colab.ipynb | google/applied-machine-learning-intensive | apache-2.0 |
Step 2: The Model
Build, fit, and evaluate a classification model. Perform any model-specific data processing that you need to perform. If the toolkit you use supports it, create visualizations for loss and accuracy improvements. Use as many text and code blocks as you need to explore the data. Note any findings.
Stude... | # Your code goes here | content/04_classification/04_classification_project/colab.ipynb | google/applied-machine-learning-intensive | apache-2.0 |
Step 3: Make Predictions and Upload To Kaggle
In this step you will make predictions on the features found in the test.csv file and upload them to Kaggle using the Kaggle API. Use as many text and code blocks as you need to explore the data. Note any findings.
Student Solution | # Your code goes here | content/04_classification/04_classification_project/colab.ipynb | google/applied-machine-learning-intensive | apache-2.0 |
What was your Kaggle score?
Record your score here
Step 4: Iterate on Your Model
In this step you're encouraged to play around with your model settings and to even try different models. See if you can get a better score. Use as many text and code blocks as you need to explore the data. Note any findings.
Student Sol... | # Your code goes here | content/04_classification/04_classification_project/colab.ipynb | google/applied-machine-learning-intensive | apache-2.0 |
문자열 배열도 가능하지면 모든 원소의 문자열 크기가 같아야 한다. 만약 더 큰 크기의 문자열을 할당하면 잘릴 수 있다. | c = np.zeros(5, dtype="S4")
c[0] = "abcd"
c[1] = "ABCDE"
c | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/2) NumPy 배열 생성과 변형.ipynb | junhwanjang/DataSchool | mit |
1이 아닌 0으로 초기화된 배열을 생성하려면 ones 명령을 사용한다. | d = np.ones((2,3,4), dtype="i8")
d | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/2) NumPy 배열 생성과 변형.ipynb | junhwanjang/DataSchool | mit |
stack 명령은 새로운 차원(축으로) 배열을 연결하며 당연히 연결하고자 하는 배열들의 크기가 모두 같아야 한다.
axis 인수(디폴트 0)를 사용하여 연결후의 회전 방향을 정한다. | np.stack([c1, c2])
np.stack([c1, c2], axis=1) | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/2) NumPy 배열 생성과 변형.ipynb | junhwanjang/DataSchool | mit |
그리드 생성
변수가 2개인 2차원 함수의 그래프를 그리거나 표를 작성하려면 많은 좌표를 한꺼번에 생성하여 각 좌표에 대한 함수 값을 계산해야 한다.
예를 들어 x, y 라는 두 변수를 가진 함수에서 x가 0부터 2까지, y가 0부터 4까지의 사각형 영역에서 변화하는 과정을 보고 싶다면 이 사각형 영역 안의 다음과 같은 (x,y) 쌍 값들에 대해 함수를 계산해야 한다.
$$ (x,y) = (0,0), (0,1), (0,2), (0,3), (0,4), (1,0), \cdots (2,4) $$
이러한 과정을 자동으로 해주는 것이 NumPy의 meshgrid 명령이다. m... | x = np.arange(3)
x
y = np.arange(5)
y
X, Y = np.meshgrid(x, y)
X
Y
[zip(x, y) for x, y in zip(X, Y)]
plt.scatter(X, Y, linewidths=10); | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/2) NumPy 배열 생성과 변형.ipynb | junhwanjang/DataSchool | mit |
トレーニングのチェックポイント
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https://www.tensorflow.org/guide/checkpoint"><img src="https://www.tensorflow.org/images/tf_logo_32px.png">TensorFlow.org で表示</a></td>
<td>Google Colab で実行</td>
<td><a target="_blank" href="https://github.com/tensorflow... | import tensorflow as tf
class Net(tf.keras.Model):
"""A simple linear model."""
def __init__(self):
super(Net, self).__init__()
self.l1 = tf.keras.layers.Dense(5)
def call(self, x):
return self.l1(x)
net = Net() | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.kerasトレーニング API から保存する
tf.kerasの保存と復元に関するガイドをご覧ください。
tf.keras.Model.save_weightsで TensorFlow チェックポイントを保存します。 | net.save_weights('easy_checkpoint') | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
チェックポイントを記述する
TensorFlow モデルの永続的な状態は、tf.Variableオブジェクトに格納されます。これらは直接作成できますが、多くの場合はtf.keras.layersやtf.keras.Modelなどの高レベル API を介して作成されます。
変数を管理する最も簡単な方法は、変数を Python オブジェクトにアタッチし、それらのオブジェクトを参照することです。
tf.train.Checkpoint、tf.keras.layers.Layerおよびtf.keras.Modelのサブクラスは、属性に割り当てられた変数を自動的に追跡します。以下の例では、単純な線形モデルを作成し、モデルのすべての変数の値を含... | def toy_dataset():
inputs = tf.range(10.)[:, None]
labels = inputs * 5. + tf.range(5.)[None, :]
return tf.data.Dataset.from_tensor_slices(
dict(x=inputs, y=labels)).repeat().batch(2)
def train_step(net, example, optimizer):
"""Trains `net` on `example` using `optimizer`."""
with tf.GradientTape() as tape... | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
チェックポイントオブジェクトを作成する
チェックポイントを手動で作成するには、tf.train.Checkpoint オブジェクトを使用します。チェックポイントを設定するオブジェクトは、オブジェクトの属性として設定されます。
tf.train.CheckpointManagerは、複数のチェックポイントの管理にも役立ちます。 | opt = tf.keras.optimizers.Adam(0.1)
dataset = toy_dataset()
iterator = iter(dataset)
ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator)
manager = tf.train.CheckpointManager(ckpt, './tf_ckpts', max_to_keep=3) | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
モデルをトレーニングおよびチェックポイントする
次のトレーニングループは、モデルとオプティマイザのインスタンスを作成し、それらをtf.train.Checkpointオブジェクトに集めます。それはデータの各バッチのループ内でトレーニングステップを呼び出し、定期的にチェックポイントをディスクに書き込みます。 | def train_and_checkpoint(net, manager):
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
print("Restored from {}".format(manager.latest_checkpoint))
else:
print("Initializing from scratch.")
for _ in range(50):
example = next(iterator)
loss = train_step(net, example, opt)
... | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
復元してトレーニングを続ける
最初のトレーニングサイクルの後、新しいモデルとマネージャーを渡すことができますが、トレーニングはやめた所から再開します。 | opt = tf.keras.optimizers.Adam(0.1)
net = Net()
dataset = toy_dataset()
iterator = iter(dataset)
ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=opt, net=net, iterator=iterator)
manager = tf.train.CheckpointManager(ckpt, './tf_ckpts', max_to_keep=3)
train_and_checkpoint(net, manager) | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
tf.train.CheckpointManagerオブジェクトは古いチェックポイントを削除します。上記では、最新の 3 つのチェックポイントのみを保持するように構成されています。 | print(manager.checkpoints) # List the three remaining checkpoints | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
これらのパス、例えば'./tf_ckpts/ckpt-10'などは、ディスク上のファイルではなく、indexファイルのプレフィックスで、変数値を含む 1 つまたはそれ以上のデータファイルです。これらのプレフィックスは、まとめて単一のcheckpointファイル('./tf_ckpts/checkpoint')にグループ化され、CheckpointManagerがその状態を保存します。 | !ls ./tf_ckpts | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
<a id="loading_mechanics"></a>
読み込みの仕組み
TensorFlowは、読み込まれたオブジェクトから始めて、名前付きエッジを持つ有向グラフを走査することにより、変数をチェックポイントされた値に合わせます。エッジ名は通常、オブジェクトの属性名に由来しており、self.l1 = tf.keras.layers.Dense(5)の"l1"などがその例です。tf.train.Checkpointは、tf.train.Checkpoint(step=...)の"step"のように、キーワード引数名を使用します。
上記の例の依存関係グラフは次のようになります。
オプティマイザは赤、通常の変数は青、オプティマイザ... | to_restore = tf.Variable(tf.zeros([5]))
print(to_restore.numpy()) # All zeros
fake_layer = tf.train.Checkpoint(bias=to_restore)
fake_net = tf.train.Checkpoint(l1=fake_layer)
new_root = tf.train.Checkpoint(net=fake_net)
status = new_root.restore(tf.train.latest_checkpoint('./tf_ckpts/'))
print(to_restore.numpy()) # We... | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
これらの新しいオブジェクトの依存関係グラフは、上で書いたより大きなチェックポイントのはるかに小さなサブグラフです。 これには、バイアスと tf.train.Checkpoint がチェックポイントに番号付けするために使用する保存カウンタのみが含まれます。
restore は、オプションのアサーションを持つステータスオブジェクトを返します。新しい Checkpoint で作成されたすべてのオブジェクトが復元されるため、status.assert_existing_objects_matched がパスとなります。 | status.assert_existing_objects_matched() | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
チェックポイントには、レイヤーのカーネルやオプティマイザの変数など、一致しない多くのオブジェクトがあります。status.assert_consumed() は、チェックポイントとプログラムが正確に一致する場合に限りパスするため、ここでは例外がスローされます。
復元延期 (Deferred restoration)
TensorFlow のLayerオブジェクトは、入力形状が利用可能な場合、最初の呼び出しまで変数の作成を遅らせる可能性があります。例えば、Denseレイヤーのカーネルの形状はレイヤーの入力形状と出力形状の両方に依存するため、コンストラクタ引数として必要な出力形状は、単独で変数を作成するために充分な情報ではありません。L... | deferred_restore = tf.Variable(tf.zeros([1, 5]))
print(deferred_restore.numpy()) # Not restored; still zeros
fake_layer.kernel = deferred_restore
print(deferred_restore.numpy()) # Restored | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
チェックポイントを手動で検査する
tf.train.load_checkpoint は、チェックポイントのコンテンツにより低いレベルのアクセスを提供する CheckpointReader を返します。これには各変数のキーからチェックポイントの各変数の形状と dtype へのマッピングが含まれます。変数のキーは上に表示されるグラフのようなオブジェクトパスです。
注意: チェックポイントへのより高いレベルの構造はありません。変数のパスと値のみが認識されており、models、layers、またはそれらがどのように接続されているかについての概念が一切ありません。 | tf.train.list_variables(tf.train.latest_checkpoint('./tf_ckpts/')) | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
net.l1.kernel の値に関心がある場合は、次のコードを使って値を取得できます。 | key = 'net/l1/kernel/.ATTRIBUTES/VARIABLE_VALUE'
print("Shape:", shape_from_key[key])
print("Dtype:", dtype_from_key[key].name) | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
また、変数の値を検査できるようにする get_tensor メソッドも提供されています。 | reader.get_tensor(key) | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
オブジェクトの追跡
self.l1 = tf.keras.layers.Dense(5)のような直接の属性割り当てと同様に、リストとディクショナリを属性に割り当てると、それらの内容を追跡します。
self.l1 = tf.keras.layers.Dense(5)のような直接の属性割り当てと同様に、リストとディクショナリを属性に割り当てると、それらの内容を追跡します。 | save = tf.train.Checkpoint()
save.listed = [tf.Variable(1.)]
save.listed.append(tf.Variable(2.))
save.mapped = {'one': save.listed[0]}
save.mapped['two'] = save.listed[1]
save_path = save.save('./tf_list_example')
restore = tf.train.Checkpoint()
v2 = tf.Variable(0.)
assert 0. == v2.numpy() # Not restored yet
restore.... | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
リストとディクショナリのラッパーオブジェクトにお気づきでしょうか。これらのラッパーは基礎的なデータ構造のチェックポイント可能なバージョンです。属性に基づく読み込みと同様に、これらのラッパーは変数の値がコンテナに追加されるとすぐにそれを復元します。 | restore.listed = []
print(restore.listed) # ListWrapper([])
v1 = tf.Variable(0.)
restore.listed.append(v1) # Restores v1, from restore() in the previous cell
assert 1. == v1.numpy() | site/ja/guide/checkpoint.ipynb | tensorflow/docs-l10n | apache-2.0 |
Leave One Out Cross Validation(LOOCV) | from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import LeaveOneOut
from sklearn.metrics import mean_squared_error
clf = LinearRegression()
loo = LeaveOneOut(len(auto_df))
#loo提供了训练和测试的索引
X = auto_df[['horsepower']].values
y = auto_df['mpg'].values
n = np.shape(X)[0]
mses =[]
for train, ... | basic/Cross-Validation and Bootstrap.ipynb | IgorWang/MachineLearningPracticer | gpl-3.0 |
$$CV_{(n)} = \frac {1} {n} \sum_{i =1}^n (\frac{y_i - \hat y_i}{1- h_i})^2$$
$$ h_i = \frac {1}{h} + \frac{(x_i - \bar x)^2}{\sum_{i'=1} ^n (x_i' - \bar x)^2 }$$ | # LOOCV 应用于同一种模型不同复杂度的选择
auto_df['horsepower^2'] = auto_df['horsepower'] * auto_df['horsepower']
auto_df['horsepower^3'] = auto_df['horsepower^2'] * auto_df['horsepower']
auto_df['horsepower^4'] = auto_df['horsepower^3'] * auto_df['horsepower']
auto_df['horsepower^5'] = auto_df['horsepower^4'] * auto_df['horsepower']
a... | basic/Cross-Validation and Bootstrap.ipynb | IgorWang/MachineLearningPracticer | gpl-3.0 |
K-Fold Cross Validation | from sklearn.cross_validation import KFold
cv_errors = []
for ncols in range(2,6):
X = auto_df[colnames[0:ncols]].values
y = auto_df['mpg'].values
kfold = KFold(len(auto_df),n_folds = 10)
mses =[]
for train,test in kfold:
Xtrain,ytrain,Xtest,ytest = X[train],y[train],X[test],y[test]
... | basic/Cross-Validation and Bootstrap.ipynb | IgorWang/MachineLearningPracticer | gpl-3.0 |
Bootstrap | from sklearn.cross_validation import Bootstrap
cv_errors = []
for ncols in range(2,6):
X = auto_df[colnames[0:ncols]].values
y = auto_df['mpg'].values
n = len(auto_df)
bs = Bootstrap(n,train_size=int(0.9*n),test_size=int(0.1*n),n_iter=10,random_state=0)
mses = []
for train,test in bs:
X... | basic/Cross-Validation and Bootstrap.ipynb | IgorWang/MachineLearningPracticer | gpl-3.0 |
One of the main things that we want to do in scientific computing is get data into and out of our programs. In addition to plain text files, there are modules that can read lots of different data formats we might encounter.
Print
We've already been using print quite a bit, but now we'll look at how to control how info... | x = 1
y = 0.0000354
z = 3.0
s = "my string"
print(x) | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
We write a string with {} embedded to indicate where variables are to be inserted. Note that {} can take arguments. We use the format() method on the string to match the variables to the {}. | print("x = {}, y = {}, z = {}, s = {}".format(x, y, z, s)) | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
Before a semi-colon, we can give an optional index/position/descriptor of the value we want to print.
After the semi-colon we give a format specifier. It has a number field and a type, like f and g to describe how floating point numbers appear and how much precision to show. Other bits are possible as well (like jus... | print("x = {0}, y = {1:10.5g}, z = {2:.3f}, s = {3}".format(x, y, z, s)) | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
there are other formatting things, like justification, etc. See the tutorial | print("{:^80}".format("centered string")) | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
File I/O
as expected, a file is an object. Here we'll use the try, except block to capture exceptions (like if the file cannot be opened). | f = open("./sample.txt", "w")
print(f)
f.write("this is my first write\n")
f.close() | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
we can easily loop over the lines in a file | f = open("./test.txt", "r")
for line in f:
print(line.split())
f.close() | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
as mentioned earlier, there are lots of string functions. Above we used strip() to remove the trailing whitespace and returns
CSV Files
comma-separated values are an easy way to exchange data -- you can generate these from a spreadsheet program. In the example below, we are assuming that the first line of the spreads... | import csv
reader = csv.reader(open("shopping.csv", "r"))
headings = None
items = []
quantity = []
unit_price = []
total = []
for row in reader:
if headings == None:
# first row
headings = row
else:
items.append(row[headings.index("item")])
quantity.append(row[headings.index(... | extra/python-io.ipynb | sbu-python-summer/python-tutorial | bsd-3-clause |
Last 24 hours: | # reading is once a minute, so take last 24 * 60 readings
def plotem(data, n=-60):
if n < 0:
start = n
end = len(data)
else:
start = 0
end = n
data[['temp', 'altitude', 'humidity']][n:].plot(subplots=True)
plotem(data, -24*60)
data.altitude[-8*60:... | posts/latest-weather-pijessie.ipynb | peakrisk/peakrisk | gpl-3.0 |
Last week | # reading is once a minute, so take last 7 * 24 * 60 readings
plotem(data, -7*24*60)
plotem(data) | posts/latest-weather-pijessie.ipynb | peakrisk/peakrisk | gpl-3.0 |
Look at all the data | data.describe()
data.tail() | posts/latest-weather-pijessie.ipynb | peakrisk/peakrisk | gpl-3.0 |
I currently have two temperature sensors:
DHT22 sensor which gives temperature and humidity.
BMP180 sensor which gives pressure and temperature.
The plot below shows the two temperature plots.
Both these sensors are currently in my study. For temperature and humidity I would like to have some readings from outside. ... | data[['temp', 'temp_dht']].plot() | posts/latest-weather-pijessie.ipynb | peakrisk/peakrisk | gpl-3.0 |
Dew Point
The warmer air is, the more moisture it can hold. The dew point is
the temperature at which air would be totally saturated if it had as
much moisture as it currently does.
Given the temperature and humidity the dew point can be calculated, the actual formula is
pretty complex.
It is explained in more detai... | data['dewpoint'] = data.temp - ((100. - data.humidity)/5.)
data[['temp', 'dewpoint', 'humidity']].plot()
data[['temp', 'dewpoint', 'humidity']].plot(subplots=True)
data[['temp', 'dewpoint']].plot()
data.altitude.plot() | posts/latest-weather-pijessie.ipynb | peakrisk/peakrisk | gpl-3.0 |
Generator
Here you'll build the generator network. The input will be our noise vector z as before. Also as before, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.
What's new here is we'll use convolutional layers to create our new images. The first layer is a full... | def generator(z, output_dim, reuse=False, alpha=0.2, training=True):
with tf.variable_scope('generator', reuse=reuse):
# First fully connected layer
x1 = tf.layers.dense(z, 4*4*512)
x1 = tf.reshape(x1, )
# Output layer, 32x32x3
logits =
out... | dcgan-svhn/DCGAN_Exercises.ipynb | brandoncgay/deep-learning | mit |
Here is what's in the file:
- 4 dimensions: station, member, time and nchar
- Variables:
- station(station) == station_id
- member(member)
- time(time) Time in s since 1.1.1970
- t2m_fc(time, member, station)
- t2m_obs(time, station)
- station_alt(station) Altitute of station in m
- station_... | # Total amount of data
rg.dimensions['station'].size * rg.dimensions['time'].size / 2
# Rough data amount per month
rg.dimensions['station'].size * rg.dimensions['time'].size / 2 / 12.
# Per station per month
rg.dimensions['time'].size / 2 / 12. | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
Ok, let's now look at some of the variables.
1.1 Time | time = rg.variables['time']
time
time[:5] | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
In fact, the time is given in seconds rather than hours. | # convert back to dates (http://unidata.github.io/netcdf4-python/#section7)
from netCDF4 import num2date
dates = num2date(time[:],units='seconds since 1970-01-01 00:00 UTC')
dates[:5] | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
So dates are in 12 hour intervals. Which means that since we downloaded 36/48h forecasts: the 12UTC dates correspond to the 36 hour fcs and the following 00UTC dates correspond to the same forecast at 48 hour lead time.
1.2 Station variables
Station and station ID are in fact the same and simply contain a number, which... | import numpy as np
# Check whether the two variables are equal
np.array_equal(rg.variables['station'][:], rg.variables['station_id'][:])
# Then just print the first 5
rg.variables['station'][:5] | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
station_alt contains the station altitude in meters. | rg.variables['station_alt'][:5]
rg.variables['station_loc'][0].data | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
Ahhhh, Aachen :D
So this leads me to believe that the station numbering is done by name. | station_lon = rg.variables['station_lon']
station_lat = rg.variables['station_lat']
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.scatter(station_lon[:], station_lat[:]) | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
Wohooo, Germany!
1.3 Temperature forecasts and observations
Ok, so now let's explore the temperature data a little. | # Let's extract the actual data from the NetCDF array
# Then we can manipulate it later.
tfc = rg.variables['t2m_fc'][:]
tobs = rg.variables['t2m_obs'][:]
tobs[:5, :5].data | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
So there are actually missing data in the observations. We will need to think about how to deal with those.
Sebastian mentioned that in the current version there is some Celcius/Kelvin inconsistencies. | plt.plot(np.mean(tfc, axis=(1, 2)))
# Since this will be fixed soon, let's just create a little ad hoc fix
idx = np.where(np.mean(tfc, axis=(1, 2)) > 100)[0][0]
tfc[idx:] = tfc[idx:] - 273.15
# Let's create a little function to visualize the ensemble forecast
# and the corresponding observation
def plot_fc_obs_hist(t... | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
2. Slicing the data
Now let's see how we can conveniently prepare the data in chunks for the post-processing purposes.
2.1 Monthly slices
The goal here is to pick all data points from a given month and also for a given time, so 00 or 12 UTC | # Let's write a handy function which returns the required data
# from the NetCDF object
def get_data_slice(rg, month, utc=0):
# Get array of datetime objects
dates = num2date(rg.variables['time'][:],
units='seconds since 1970-01-01 00:00 UTC')
# Extract months and hours
months = np.... | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
3. Compute the parametric and sample CRPS for the raw ensemble data
3.1 CRPS for a normal distribution
From Gneiting et al. 2005, EMOS: | from scipy.stats import norm
def crps_normal(mu, sigma, y):
loc = (y - mu) / sigma
crps = sigma * (loc * (2 * norm.cdf(loc) - 1) +
2 * norm.pdf(loc) - 1. / np.sqrt(np.pi))
return crps
# Get ensmean and ensstd
tfc_jan_00_mean = np.mean(tfc_jan_00, axis=1)
tfc_jan_00_std = np.std(tfc_ja... | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
Nice, this corresponds well to the value sebastian got for the raw ensemble in January.
3.2 Sample CRPS
For this we use the scoringRules package inside enstools. | import sys
sys.path.append('/Users/stephanrasp/repositories/enstools')
from enstools.scores.ScoringRules2Py.scoringtools import
??enstools.scores.crps_sample
tfc_jan_00.shape
tobs_jan_00.shape
tfc_jan_00_flat = np.rollaxis(tfc_jan_00, 1, 0)
tfc_jan_00_flat.shape
tfc_jan_00_flat = tfc_jan_00_flat.reshape(tfc_jan_0... | data_exploration/python_data_handling.ipynb | slerch/ppnn | mit |
Configuration
This configuration determines whether functions print logs during the execution. | debugMode = True | applications/notebooks/laurens/comparisons.ipynb | phenology/infrastructure | apache-2.0 |
Connect to Spark
Here, the Spark context is loaded, which allows for a connection to HDFS. | appName = "plot_GeoTiff"
masterURL = "spark://emma0.emma.nlesc.nl:7077"
#A context needs to be created if it does not already exist
try:
sc.stop()
except NameError:
print("A new Spark Context will be created.")
sc = SparkContext(conf = SparkConf().setAppName(appName).setMaster(masterURL))
conf = sc.getConf() | applications/notebooks/laurens/comparisons.ipynb | phenology/infrastructure | apache-2.0 |
Subtitle | def getModeAsArray(filePath):
data = sc.binaryFiles(filePath).take(1)
byteArray = bytearray(data[0][1])
memfile = MemoryFile(byteArray)
dataset = memfile.open()
array = np.array(dataset.read()[0], dtype=np.float64)
memfile.close()
array = array.flatten()
array = array[~np.isnan(array)]
... | applications/notebooks/laurens/comparisons.ipynb | phenology/infrastructure | apache-2.0 |
BloomFinalLowPR and LeafFinalLowPR | array1 = getModeAsArray("hdfs:///user/emma/svd/BloomFinalLowPRLeafFinalLowPR/ModeU01.tif")
array2 = getModeAsArray("hdfs:///user/emma/svd/spark/BloomFinalLowPRLeafFinalLowPR3/u_tiffs/svd_u_0_3.tif")
detemineNorm(array1, array2)
array1 = getModeAsArray("hdfs:///user/emma/svd/BloomFinalLowPRLeafFinalLowPR/ModeU02.tif")
... | applications/notebooks/laurens/comparisons.ipynb | phenology/infrastructure | apache-2.0 |
BloomGridmet and LeafGridmet | for i in range(37):
if (i < 9):
path1 = "hdfs:///user/emma/svd/BloomGridmetLeafGridmetCali/ModeU0"+ str(i+1) + ".tif"
else:
path1 = "hdfs:///user/emma/svd/BloomGridmetLeafGridmetCali/ModeU"+ str(i+1) + ".tif"
array1 = getModeAsArray(path1)
array2 = getModeAsArray("hdfs:///user/emma/svd/s... | applications/notebooks/laurens/comparisons.ipynb | phenology/infrastructure | apache-2.0 |
\begin{equation}
A = \left( \begin{array}{rrr}
0.01 & 0.0012 & 0.000 \
1.00 & 99.9000 & 0.010 \
1.20 & 999999.1230 & 0.001 \
\end{array} \right)
\end{equation} | writer = pytablewriter.LatexMatrixWriter()
writer.table_name = "B"
writer.value_matrix = [
["a_{11}", "a_{12}", "\\ldots", "a_{1n}"],
["a_{21}", "a_{22}", "\\ldots", "a_{2n}"],
[r"\vdots", "\\vdots", "\\ddots", "\\vdots"],
["a_{n1}", "a_{n2}", "\\ldots", "a_{nn}"],
]
writer.write_table() | test/data/pytablewriter_examples.ipynb | thombashi/sqlitebiter | mit |
\begin{equation}
B = \left( \begin{array}{llll}
a_{11} & a_{12} & \ldots & a_{1n} \
a_{21} & a_{22} & \ldots & a_{2n} \
\vdots & \vdots & \ddots & \vdots \
a_{n1} & a_{n2} & \ldots & a_{nn} \
\end{array} \right)
\end{equation} | writer = pytablewriter.LatexTableWriter()
writer.header_list = header_list
writer.value_matrix = data
writer.write_table() | test/data/pytablewriter_examples.ipynb | thombashi/sqlitebiter | mit |
\begin{array}{r | r | l | l | l | l} \hline
\verb|int| & \verb|float| & \verb|str| & \verb|bool| & \verb|mix| & \verb|time| \ \hline
\hline
0 & 0.10 & hoge & True & 0 & \verb|2017-01-01 03:04:05+0900| \ \hline
2 & -2.23 & foo & False & & \verb|2017-12-23 12:34:51+0900| \ \hline
3 & 0.00 & bar & Tru... | writer = pytablewriter.MarkdownTableWriter()
writer.table_name = table_name
writer.header_list = header_list
writer.value_matrix = data
writer.write_table()
writer = pytablewriter.MarkdownTableWriter()
writer.table_name = "write example with a margin"
writer.header_list = header_list
writer.value_matrix = data
writer... | test/data/pytablewriter_examples.ipynb | thombashi/sqlitebiter | mit |
As you can see the function can also walk the class hierarchy, so the check is not so trivial like the one you would obtain by directly using type().
The isinstance() function, however, does not completely solve the problem. If we write a class that actually acts like a list but does not inherit from it, isinstance() d... | class MyList:
pass
ml = MyList()
isinstance(ml, list) | notebooks/giordani/Python_3_OOP_Part_6__Abstract_Base_Classes.ipynb | Heroes-Academy/OOP_Spring_2016 | mit |
since isinstance() does not check the content of the class or its behaviour, it just consider the class and its ancestors.
The problem, thus, may be summed up with the following question: what is the best way to test that an object exposes a given interface? Here, the word interface is used for its natural meaning, wit... | from abc import ABCMeta
class MyABC(metaclass=ABCMeta):
pass
MyABC.register(tuple)
assert issubclass(tuple, MyABC)
assert isinstance((), MyABC) | notebooks/giordani/Python_3_OOP_Part_6__Abstract_Base_Classes.ipynb | Heroes-Academy/OOP_Spring_2016 | mit |
Here, the MyABC class is provided the ABCMeta metaclass. This puts the two __isinstancecheck__() and __subclasscheck__() methods inside MyABC so that, when issuing isinstance(), what Python actually ececutes is | d = {'a': 1}
isinstance(d, MyABC)
MyABC.__class__.__instancecheck__(MyABC, d)
isinstance((), MyABC)
MyABC.__class__.__instancecheck__(MyABC, ()) | notebooks/giordani/Python_3_OOP_Part_6__Abstract_Base_Classes.ipynb | Heroes-Academy/OOP_Spring_2016 | mit |
After the definition of MyABC we need a way to signal that a given class is an instance of the Abstract Base Class and this happens through the register() method, provided by the ABCMeta metaclass. Calling MyABC.register(tuple) we record inside MyABC the fact that the tuple class shall be identified as a subclass of My... | MyABC._abc_registry.data | notebooks/giordani/Python_3_OOP_Part_6__Abstract_Base_Classes.ipynb | Heroes-Academy/OOP_Spring_2016 | mit |
Sigmoid
The sigmoid function was once the default choice of activation function when building a network and to some extent it still is. By mapping values into a range between 0 and 1 it lacks the beneficial quality of being zero centered - a property that aids gradient descent during back propogation. | def activation_sigmoid(x, derivative):
sigmoid_value = 1/(1+np.exp(-x))
if not derivative:
return sigmoid_value
else:
return sigmoid_value*(1-sigmoid_value) | neural-networks/defining_activation_functions.ipynb | tpin3694/tpin3694.github.io | mit |
When plotted on a range of -5,5, this gives the following shape. | x_values = np.arange(-5, 6, 0.1)
y_sigmoid = activation_sigmoid(x_values, derivative=False)
plt.plot(x_values, y_sigmoid) | neural-networks/defining_activation_functions.ipynb | tpin3694/tpin3694.github.io | mit |
Tanh
tanh is very similar in shape to the sigmoid, however the defining difference is that tanh ranges from -1 to 1, making it zero centered and consequently a very popular choice. Conveniently, tanh is pre-defined in NumPy, however it is still worthwhile wrapping it up in a function in order to define the derivative o... | def activation_tanh(x, derivative):
tanh_value = np.tanh(x)
if not derivative:
return tanh_value
else:
return 1-tanh_value**2
y_tanh = activation_tanh(x_values, derivative = False)
plt.plot(x_values, y_tanh) | neural-networks/defining_activation_functions.ipynb | tpin3694/tpin3694.github.io | mit |
ReLU
The Rectified Linear Unit is another commonly used activation function with a range from 0 to infinity. A major advantage of the ReLU function is that, unlike the sigmoid and tanh, the gradient of the ReLU function does not vanish as the limits are approached. An additionaly benefit of the ReLU is its enhanced com... | def relu_activation(x, derivative):
if not derivative:
return x * (x>0)
else:
x[x <= 0] = 0
x[x > 0] = 1
return x
y_relu = relu_activation(x_values, derivative=False)
plt.plot(x_values, y_relu) | neural-networks/defining_activation_functions.ipynb | tpin3694/tpin3694.github.io | mit |
It is probably worth noting, that the leaky ReLU is a closely related function with the only difference being that values < 0 are not completely set to 0, instead multiplied by 0.01.
Softmax
The final function to be discussed is the softmax, a function typically used in the final layer of a network. The softmax functio... | def softmax_activation(x):
exponent = np.exp(x - np.max(x))
softmax_value = exponent/np.sum(exponent, axis = 0)
return softmax_value
y_softmax = softmax_activation(x_values)
plt.plot(x_values, y_softmax)
print("The sum of all softmax probabilities can be confirmed as " + str(np.sum(y_softmax))) | neural-networks/defining_activation_functions.ipynb | tpin3694/tpin3694.github.io | mit |
Initializing Python | #!/usr/bin/env python
# -*- coding: UTF-8
# IMPORTING KEY PACKAGES
import csv # for reading in CSVs and turning them into dictionaries
import re # for regular expressions
import os # for navigating file trees
import nltk # for natural language processing tools
import pandas # for working with dataframes
import numpy a... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Reading in preliminary data | sample = [] # make empty list
with open('../data_URAP_etc/mission_data_prelim.csv', 'r', encoding = 'Latin-1')\
as csvfile: # open file
reader = csv.DictReader(csvfile) # create a reader
for row in reader: # loop through rows
sample.append(row) # append each row to the list
sample... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Descriptive statistics
How urban proximity is coded: Lower number = more urban (closer to large city)
More specifically, it uses two digits with distinct meanings:
- the first digit:
- 1 = city
- 2 = suburb
- 3 = town
- 4 = rural
- the second digit:
- 1 = large or fringe
- 2 = mid-size or dist... | print(df.describe()) # get descriptive statistics for all numerical columns
print()
print(df['ULOCAL'].value_counts()) # frequency counts for categorical data
print()
print(df['LEVEL'].value_counts()) # treat grade range served as categorical
# Codes for level/ grade range served: 3 = High school, 2 = Middle school, 1 ... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
What these numbers say about the charter schools in the sample:
Most are located in large cities, followed by large suburbs, then medium and small city, and then rural.
The means for percent minorities and students receiving free- or reduced-price lunch are both about 60%.
Most are in CA, TX, AZ, and FL
Most of the sc... | # Now we clean the webtext by rendering each word lower-case then removing punctuation.
df['webtext_lc'] = df['WEBTEXT'].str.lower() # make the webtext lower case
df['webtokens'] = df['webtext_lc'].apply(nltk.word_tokenize) # tokenize the lower-case webtext by word
df['webtokens_nopunct'] = df['webtokens'].apply(lambd... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Counting document lengths | # We can also count document lengths. I'll mostly use the version with punctuation removed but including stopwords,
# because stopwords are also part of these schools' public image/ self-presentation to potential parents, regulators, etc.
df['webstem_count'] = df['webtokens_stemmed'].apply(len) # find word count witho... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
(Excessively) Frequent words | # First, aggregate all the cleaned webtext:
webtext_all = []
df['webtokens_clean'].apply(lambda x: [webtext_all.append(word) for word in x])
webtext_all[:20]
# Now apply the nltk function FreqDist to count the number of times each token occurs.
word_frequency = nltk.FreqDist(webtext_all)
#print out the 50 most freque... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
### These are prolific, ritual, empty words and will be excluded from topic models!
Distinctive words (mostly place names) | sklearn_dtm = countvec.fit_transform(df['webtext_stemmed'])
print(sklearn_dtm)
# What are some of the words in the DTM?
print(countvec.get_feature_names()[:10])
# now we can create the dtm, but with cells weigthed by the tf-idf score.
dtm_tfidf_df = pandas.DataFrame(tfidfvec.fit_transform(df.webtext_stemmed).toarray... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Like the frequent words above, these highly "unique" words are empty of meaning and will be excluded from topic models!
Word Embeddings with word2vec
Word2Vec features
<ul>
<li>Size: Number of dimensions for word embedding model</li>
<li>Window: Number of context words to observe in each direction</li>
<li>min_count: M... | # train the model, using a minimum of 5 words
model = gensim.models.Word2Vec(words_by_sentence, size=100, window=5, \
min_count=2, sg=1, alpha=0.025, iter=5, batch_words=10000, workers=1)
# dictionary of words in model (may not work for old gensim)
# print(len(model.vocab))
# model.vocab... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Binary of essentialist (top-left) and progressivist (bottom-right) word vectors
Topic Modeling with scikit-learn
For documentation on this topic modeling (TM) package, which uses Latent Dirichlet Allocation (LDA), see here.
And for documentation on the vectorizer package, CountVectorizer from scikit-learn, see here. | ####Adopted From:
#Author: Olivier Grisel <olivier.grisel@ensta.org>
# Lars Buitinck
# Chyi-Kwei Yau <chyikwei.yau@gmail.com>
# License: BSD 3 clause
# Initialize the variables needed for the topic models
n_samples = 2000
n_topics = 3
n_top_words = 50
# Create helper function that prints out the top ... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
These topics seem to mean:
- topic 0 relates to GOALS,
- topic 1 relates to CURRICULUM, and
- topic 2 relates to PHILOSOPHY or learning process (but this topic less clear/ more mottled) | # Preparation for looking at distribution of topics over schools
topic_dist = lda.transform(tf) # transpose topic distribution
topic_dist_df = pandas.DataFrame(topic_dist) # turn into a df
df_w_topics = topic_dist_df.join(df) # merge with charter MS dataframe
df_w_topics[:20] # check out the merged df with topics!
top... | scripts/analysis_prelim.ipynb | jhaber-zz/Charter-school-identities | mit |
Import data | features = pd.read_csv('train_values.csv')
labels = pd.read_csv('train_labels.csv')
xlab = 'serum_cholesterol_mg_per_dl'
ylab = 'resting_blood_pressure'
print(labels.head())
features.head()
cluster_arr = np.array(features[[xlab,ylab]]).reshape(-1,2)
cluster_arr[:5] | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Cluster subsample visualization | x = features['serum_cholesterol_mg_per_dl']
y = features['resting_blood_pressure']
trace = [go.Scatter(
x = x,
y = y,
name = 'data',
mode = 'markers',
hoverinfo = 'text',
text = ['x: %s<br>y: %s' % (x_i, y_i) for x_i, y_i in zip(x, y)]
)]
layout = go.Layout(
xaxis = dict({'title': xlab}),
... | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Hierarchical Clustering
https://scikit-learn.org/stable/modules/clustering.html
https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster
https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/ | from scipy.cluster.hierarchy import dendrogram, linkage | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Single Link | plt.figure(figsize=(15, 7))
linked = linkage(cluster_arr, 'single')
# labelList = range(1, 11)
dendrogram(linked,
orientation='top',
# labels=labelList,
distance_sort='descending',
show_leaf_counts=True)
plt.show() | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Complete Link | plt.figure(figsize=(15, 7))
linked = linkage(cluster_arr, 'complete')
# labelList = range(1, 11)
dendrogram(linked,
orientation='top',
# labels=labelList,
distance_sort='descending',
show_leaf_counts=True)
plt.show() | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Average Link | plt.figure(figsize=(15, 7))
linked = linkage(cluster_arr, 'average')
# labelList = range(1, 11)
dendrogram(linked,
orientation='top',
# labels=labelList,
distance_sort='descending',
show_leaf_counts=True)
plt.show() | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Ward Variance | plt.figure(figsize=(15, 7))
linked = linkage(cluster_arr, 'ward')
# labelList = range(1, 11)
dendrogram(linked,
orientation='top',
# labels=labelList,
distance_sort='descending',
show_leaf_counts=True)
plt.show() | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Density-based clustering
DBSCAN | from sklearn.cluster import DBSCAN
clustering = DBSCAN(eps=3, min_samples=2).fit(cluster_arr)
clustering
y_pred = clustering.labels_
y_pred
x = cluster_arr[:, 0]
y = cluster_arr[:, 1]
# col = ['#F33' if i == 1 else '#33F' for i in y_pred]
trace = [go.Scatter(
x = x,
y = y,
marker = dict(
# co... | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Other based on DBSCAN
K-Means | from sklearn.cluster import KMeans
y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(cluster_arr)
y_pred
x = cluster_arr[:, 0]
y = cluster_arr[:, 1]
# col = ['#F33' if i == 1 else '#33F' for i in y_pred]
trace = [go.Scatter(
x = x,
y = y,
marker = dict(
# color = col,
... | kaggle/machine-learning-with-a-heart/Lab5.ipynb | xR86/ml-stuff | mit |
Dates
For both filtering and output, it is often necessary to parse and/or normalize the created_at date. The following shows the original created_at date and the date as an ISO 8601 date. | !head -n5 tweets.json | jq -c '[.created_at, .created_at | strptime("%A %B %d %T %z %Y") | todate]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Filtering
Filtering text
Case sensitive | !cat tweets.json | jq -c 'select(.text | contains("blog")) | [.id_str, .text]'
!cat tweets.json | jq -c 'select(.text | contains("BLOG")) | [.id_str, .text]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Case insensitive
To ignore case, use a regular expression filter with the case-insensitive flag. | !cat tweets.json | jq -c 'select(.text | test("BLog"; "i")) | [.id_str, .text]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Filtering on multiple terms (OR) | !cat tweets.json | jq -c 'select(.text | test("BLog|twarc"; "i")) | [.id_str, .text]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Filtering on multiple terms (AND) | !cat tweets.json | jq -c 'select((.text | test("BLog"; "i")) and (.text | test("twitter"; "i"))) | [.id_str, .text]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Filter dates
The following shows tweets created after November 5, 2016. | !cat tweets.json | jq -c 'select((.created_at | strptime("%A %B %d %T %z %Y") | mktime) > ("2016-11-05T00:00:00Z" | fromdateiso8601)) | [.id_str, .created_at, (.created_at | strptime("%A %B %d %T %z %Y") | todate)]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Is retweet | !cat tweets.json | jq -c 'select(has("retweeted_status")) | [.id_str, .retweeted_status.id]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Is quote | !cat tweets.json | jq -c 'select(has("quoted_status")) | [.id_str, .quoted_status.id]' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Output
To write output to a file use > <filename>. For example: cat tweets.json | jq -r '.id_str' > tweet_ids.txt
CSV
Following is a CSV output that has fields similar to the CSV output produced by SFM's export functionality.
Note that is uses the -r flag for jq instead of the -c flag.
Also note that is it ... | !head -n5 tweets.json | jq -r '[(.created_at | strptime("%A %B %d %T %z %Y") | todate), .id_str, .user.screen_name, .user.followers_count, .user.friends_count, .retweet_count, .favorite_count, .in_reply_to_screen_name, "http://twitter.com/" + .user.screen_name + "/status/" + .id_str, (.text | gsub("\n";" ")), has("retw... | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Header row
The header row should be written to the output file with > before appending the CSV with >>. | !echo "[]" | jq -r '["created_at","twitter_id","screen_name","followers_count","friends_count","retweet_count","favorite_count","in_reply_to_screen_name","twitter_url","text","is_retweet","is_quote"] | @csv' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
Splitting files
Excel can load CSV files with over a million rows. Howver, for practical purposes a much smaller number is recommended.
The following uses the split command to split the CSV output into multiple files. Note that the flags accepted may be different in your environment.
cat tweets.json | jq -r '[.id_str, ... | !head -n5 tweets.json | jq -r '.id_str' | 20161122-twitter-jq-recipes/twitter_jq_recipes.ipynb | gwu-libraries/notebooks | mit |
HDMI Frontend
The HDMI frontend modules wrap all of the clock and timing logic. The HDMI input frontend can be used independently from the rest of the pipeline by accessing its driver from the base overlay. | hdmiin_frontend = base.video.hdmi_in.frontend | boards/Pynq-Z1/base/notebooks/video/hdmi_video_pipeline.ipynb | cathalmccabe/PYNQ | bsd-3-clause |
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