markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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We double-checked it by printing info from Thurmond, whom was part of the senate but appeared as if he had
served 26 periods of 6 years each (26*6 IMPOSIBLE!)
Thurmond = df[df['lastname'] == 'Thurmond']
Thurmond
11) Who has served for more years?
Senators = 6-year terms BUT the data we have is for 2-year terms
Represen... | terms_served_by_senators= senator.groupby('complete_name')['bioguide'].value_counts()
years= terms_served_by_senators * 2
total_years_served = years.sort_values(ascending=False)
pd.DataFrame(total_years_served)
terms_served_by_representative= representative.groupby("complete_name")['bioguide'].value_counts()
year... | foundations_hw/08/Homework8_benzaquen_congress_data.ipynb | mercybenzaquen/foundations-homework | mit |
12)The most popular name in congress is.... | df['firstname'].value_counts()
#this might be counting the same person many times but still we can get an idea of what names are more popular | foundations_hw/08/Homework8_benzaquen_congress_data.ipynb | mercybenzaquen/foundations-homework | mit |
Make three charts with your dataset
1) Distribution of age | plt.style.use("ggplot")
df['age'].hist(bins=15, xlabelsize=12, ylabelsize=12, color=['y'])
df.head(20).sort_values(by='age',ascending=True).plot(kind='barh', x=['complete_name'], y='age', color="y")
df.plot.scatter(x='congress', y='age');
df.plot.hexbin(x='age', y='congress', gridsize=25, legend=True) | foundations_hw/08/Homework8_benzaquen_congress_data.ipynb | mercybenzaquen/foundations-homework | mit |
As always, let's do imports and initialize a logger and a new Bundle. | import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary() | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
Passband Options
Passband options follow the exact same rules as dataset columns.
Sending a single value to the argument will apply it to each component in which the time array is attached (either based on the list of components sent or the defaults from the dataset method).
Note that for light curves, in particular, t... | b.add_dataset('lc',
times=[0,1],
dataset='lc01',
overwrite=True)
print(b.get_parameter(qualifier='times', dataset='lc01'))
print(b.filter(qualifier='ld_mode', dataset='lc01')) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
As you might expect, if you want to pass different values to different components, simply provide them in a dictionary. | b.add_dataset('lc',
times=[0,1],
ld_mode='manual',
ld_func={'primary': 'logarithmic', 'secondary': 'quadratic'},
dataset='lc01',
overwrite=True)
print(b.filter(qualifier='ld_func', dataset='lc01')) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
Note here that we didn't explicitly override the defaults for '_default', so they used the phoebe-wide defaults. If you wanted to set a value for the ld_coeffs of any star added in the future, you would have to provide a value for '_default' in the dictionary as well. | print(b.filter(qualifier'ld_func@lc01', check_default=False)) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
This syntax may seem a bit bulky - but alternatively you can add the dataset without providing values and then change the values individually using dictionary access or set_value.
Adding a Dataset from a File
Manually from Arrays
For now, the only way to load data from a file is to do the parsing externally and pass th... | times, fluxes, sigmas = np.loadtxt('test.lc.in', unpack=True)
b.add_dataset('lc',
times=times,
fluxes=fluxes,
sigmas=sigmas,
dataset='lc01',
overwrite=True) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
Enabling and Disabling Datasets
See the Compute Tutorial
Dealing with Phases
Datasets will no longer accept phases. It is the user's responsibility to convert
phased data into times given an ephemeris. But it's still useful to be able to
convert times to phases (and vice versa) and be able to plot in phase.
Those con... | print(b.get_ephemeris())
print(b.to_phase(0.0))
print(b.to_time(-0.25)) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
All of these by default use the period in the top-level of the current hierarchy,
but accept a component keyword argument if you'd like the ephemeris of an
inner-orbit or the rotational ephemeris of a star in the system.
We'll see how plotting works later, but if you manually wanted to plot the dataset
with phases, all... | print(b.to_phase(b.get_value(qualifier='times'))) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
or | print(b.to_phase('times@lc01')) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
Although it isn't possible to attach data in phase-space, it is possible to tell PHOEBE at which phases to compute the model by setting compute_phases. Note that this overrides the value of times when the model is computed. | b.add_dataset('lc',
compute_phases=np.linspace(0,1,11),
dataset='lc01',
overwrite=True) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
The usage of compute_phases (as well as compute_times) will be discussed in further detail in the compute tutorial and the advanced: compute times & phases tutorial.
Note also that although you can pass compute_phases directly to add_dataset, if you do not, it will be constrained by compute_times by default. In this... | b.add_dataset('lc',
times=[0],
dataset='lc01',
overwrite=True)
print(b['compute_phases@lc01'])
b.flip_constraint('compute_phases', dataset='lc01', solve_for='compute_times')
b.set_value('compute_phases', dataset='lc01', value=np.linspace(0,1,101)) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
Removing Datasets
Removing a dataset will remove matching parameters in either the dataset, model, or constraint contexts. This action is permanent and not undo-able via Undo/Redo. | print(b.datasets) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
The simplest way to remove a dataset is by its dataset tag: | b.remove_dataset('lc01')
print(b.datasets) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
But remove_dataset also takes any other tag(s) that could be sent to filter. | b.remove_dataset(kind='rv')
print(b.datasets) | development/tutorials/datasets_advanced.ipynb | phoebe-project/phoebe2-docs | gpl-3.0 |
TimeDistributed
[wrappers.TimeDistributed.0] wrap a Dense layer with units 4 (input: 3 x 6) | data_in_shape = (3, 6)
layer_0 = Input(shape=data_in_shape)
layer_1 = TimeDistributed(Dense(4))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np.random.seed(4000 + i)
weights.append(2 * np... | notebooks/layers/wrappers/TimeDistributed.ipynb | qinwf-nuan/keras-js | mit |
[wrappers.TimeDistributed.1] wrap a Conv2D layer with 6 3x3 filters (input: 5x4x4x2) | data_in_shape = (5, 4, 4, 2)
layer_0 = Input(shape=data_in_shape)
layer_1 = TimeDistributed(Conv2D(6, (3,3), data_format='channels_last'))(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
np.rand... | notebooks/layers/wrappers/TimeDistributed.ipynb | qinwf-nuan/keras-js | mit |
export for Keras.js tests | print(json.dumps(DATA)) | notebooks/layers/wrappers/TimeDistributed.ipynb | qinwf-nuan/keras-js | mit |
Load and process review dataset
For this assignment, we will use the same subset of the Amazon product review dataset that we used in Module 3 assignment. The subset was chosen to contain similar numbers of positive and negative reviews, as the original dataset consisted of mostly positive reviews. | products = graphlab.SFrame('amazon_baby_subset.gl/') | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Just like we did previously, we will work with a hand-curated list of important words extracted from the review data. We will also perform 2 simple data transformations:
Remove punctuation using Python's built-in string manipulation functionality.
Compute word counts (only for the important_words)
Refer to Module 3 a... | import json
with open('important_words.json', 'r') as f:
important_words = json.load(f)
important_words = [str(s) for s in important_words]
# Remote punctuation
def remove_punctuation(text):
import string
return text.translate(None, string.punctuation)
products['review_clean'] = products['review'].apply... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
The SFrame products now contains one column for each of the 193 important_words. | products | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Split data into training and validation sets
We will now split the data into a 90-10 split where 90% is in the training set and 10% is in the validation set. We use seed=1 so that everyone gets the same result. | train_data, validation_data = products.random_split(.9, seed=1)
print 'Training set : %d data points' % len(train_data)
print 'Validation set: %d data points' % len(validation_data) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Convert SFrame to NumPy array
Just like in the earlier assignments, we provide you with a function that extracts columns from an SFrame and converts them into a NumPy array. Two arrays are returned: one representing features and another representing class labels.
Note: The feature matrix includes an additional column ... | import numpy as np
def get_numpy_data(data_sframe, features, label):
data_sframe['intercept'] = 1
features = ['intercept'] + features
features_sframe = data_sframe[features]
feature_matrix = features_sframe.to_numpy()
label_sarray = data_sframe[label]
label_array = label_sarray.to_numpy()
r... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Note that we convert both the training and validation sets into NumPy arrays.
Warning: This may take a few minutes. | feature_matrix_train, sentiment_train = get_numpy_data(train_data, important_words, 'sentiment')
feature_matrix_valid, sentiment_valid = get_numpy_data(validation_data, important_words, 'sentiment') | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Are you running this notebook on an Amazon EC2 t2.micro instance? (If you are using your own machine, please skip this section)
It has been reported that t2.micro instances do not provide sufficient power to complete the conversion in acceptable amount of time. For interest of time, please refrain from running get_nump... | '''
produces probablistic estimate for P(y_i = +1 | x_i, w).
estimate ranges between 0 and 1.
'''
def predict_probability(feature_matrix, coefficients):
# Take dot product of feature_matrix and coefficients
score = np.dot(feature_matrix, coefficients)
# Compute P(y_i = +1 | x_i, w) using the link fun... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Derivative of log likelihood with respect to a single coefficient
Let us now work on making minor changes to how the derivative computation is performed for logistic regression.
Recall from the lectures and Module 3 assignment that for logistic regression, the derivative of log likelihood with respect to a single coeff... | def feature_derivative(errors, feature):
# Compute the dot product of errors and feature
## YOUR CODE HERE
derivative = np.dot(errors, feature)
return derivative | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Note. We are not using regularization in this assignment, but, as discussed in the optional video, stochastic gradient can also be used for regularized logistic regression.
To verify the correctness of the gradient computation, we provide a function for computing average log likelihood (which we recall from the last as... | def compute_avg_log_likelihood(feature_matrix, sentiment, coefficients):
indicator = (sentiment==+1)
scores = np.dot(feature_matrix, coefficients)
logexp = np.log(1. + np.exp(-scores))
# Simple check to prevent overflow
mask = np.isinf(logexp)
logexp[mask] = -scores[mask]
lp =... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question: Recall from the lecture and the earlier assignment, the log likelihood (without the averaging term) is given by
$$\ell\ell(\mathbf{w}) = \sum_{i=1}^N \Big( (\mathbf{1}[y_i = +1] - 1)\mathbf{w}^T h(\mathbf{x}_i) - \ln\left(1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))\right) \Big) $$
How are the functions $\el... | j = 1 # Feature number
i = 10 # Data point number
coefficients = np.zeros(194) # A point w at which we are computing the gradient.
predictions = predict_probability(feature_matrix_train[i:i+1,:], coefficients)
indicator = (sentiment_train[i:i+1]==+1)
errors = indicator - p... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question: The code block above computed $\partial\ell_{\color{red}{i}}(\mathbf{w})/{\partial w_j}$ for j = 1 and i = 10. Is $\partial\ell_{\color{red}{i}}(\mathbf{w})/{\partial w_j}$ a scalar or a 194-dimensional vector?
Modifying the derivative for using a batch of data points
Stochastic gradient estimates the a... | j = 1 # Feature number
i = 10 # Data point start
B = 10 # Mini-batch size
coefficients = np.zeros(194) # A point w at which we are computing the gradient.
predictions = predict_probability(feature_matrix_train[i:i+B,:], coefficients)
indicator = (senti... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question: The code block above computed
$\color{red}{\sum_{s = i}^{i+B}}\partial\ell_{s}(\mathbf{w})/{\partial w_j}$
for j = 10, i = 10, and B = 10. Is this a scalar or a 194-dimensional vector?
Quiz Question: For what value of B is the term
$\color{red}{\sum_{s = 1}^{B}}\partial\ell_{s}(\mathbf{w})/\partial w_... | print len(sentiment_train) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Averaging the gradient across a batch
It is a common practice to normalize the gradient update rule by the batch size B:
$$
\frac{\partial\ell_{\color{red}{A}}(\mathbf{w})}{\partial w_j} \approx \color{red}{\frac{1}{B}} {\sum_{s = i}^{i + B}} h_j(\mathbf{x}_s)\left(\mathbf{1}[y_s = +1] - P(y_s = +1 | \mathbf{x}_s, \mat... | from math import sqrt
def logistic_regression_SG(feature_matrix, sentiment, initial_coefficients, step_size, batch_size, max_iter):
log_likelihood_all = []
# make sure it's a numpy array
coefficients = np.array(initial_coefficients)
# set seed=1 to produce consistent results
np.random.seed(seed... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Note. In practice, the final set of coefficients is rarely used; it is better to use the average of the last K sets of coefficients instead, where K should be adjusted depending on how fast the log likelihood oscillates around the optimum.
Checkpoint
The following cell tests your stochastic gradient ascent function usi... | sample_feature_matrix = np.array([[1.,2.,-1.], [1.,0.,1.]])
sample_sentiment = np.array([+1, -1])
coefficients, log_likelihood = logistic_regression_SG(sample_feature_matrix, sample_sentiment, np.zeros(3),
step_size=1., batch_size=2, max_iter=2)
print '----------------... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Compare convergence behavior of stochastic gradient ascent
For the remainder of the assignment, we will compare stochastic gradient ascent against batch gradient ascent. For this, we need a reference implementation of batch gradient ascent. But do we need to implement this from scratch?
Quiz Question: For what value of... | coefficients, log_likelihood = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
step_size=5e-1, batch_size=1, max_iter=10) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question. When you set batch_size = 1, as each iteration passes, how does the average log likelihood in the batch change?
* Increases
* Decreases
* Fluctuates
Now run batch gradient ascent over the feature_matrix_train for 200 iterations using:
* initial_coefficients = np.zeros(194)
* step_size = 5e-1
* batch_siz... | # YOUR CODE HERE
coefficients_batch, log_likelihood_batch = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
step_size=5e-1,
batch_size = len(feature_... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question. When you set batch_size = len(train_data), as each iteration passes, how does the average log likelihood in the batch change?
* Increases
* Decreases
* Fluctuates
Make "passes" over the dataset
To make a fair comparison betweeen stochastic gradient ascent and batch gradient ascent, we measure the avera... | # number of passes is number to complete the whole dataset
# For each batch size, we update 1 gradient, so
2*(50000/100) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Log likelihood plots for stochastic gradient ascent
With the terminology in mind, let us run stochastic gradient ascent for 10 passes. We will use
* step_size=1e-1
* batch_size=100
* initial_coefficients to all zeros. | step_size = 1e-1
batch_size = 100
num_passes = 10
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
We provide you with a utility function to plot the average log likelihood as a function of the number of passes. | import matplotlib.pyplot as plt
%matplotlib inline
def make_plot(log_likelihood_all, len_data, batch_size, smoothing_window=1, label=''):
plt.rcParams.update({'figure.figsize': (9,5)})
log_likelihood_all_ma = np.convolve(np.array(log_likelihood_all), \
np.ones((smoothing... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Smoothing the stochastic gradient ascent curve
The plotted line oscillates so much that it is hard to see whether the log likelihood is improving. In our plot, we apply a simple smoothing operation using the parameter smoothing_window. The smoothing is simply a moving average of log likelihood over the last smoothing_w... | make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,
smoothing_window=30, label='stochastic gradient, step_size=1e-1') | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Checkpoint: The above plot should look smoother than the previous plot. Play around with smoothing_window. As you increase it, you should see a smoother plot.
Stochastic gradient ascent vs batch gradient ascent
To compare convergence rates for stochastic gradient ascent with batch gradient ascent, we call make_plot() m... | step_size = 1e-1
batch_size = 100
num_passes = 200
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
## YOUR CODE HERE
coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
We compare the convergence of stochastic gradient ascent and batch gradient ascent in the following cell. Note that we apply smoothing with smoothing_window=30. | make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,
smoothing_window=30, label='stochastic, step_size=1e-1')
make_plot(log_likelihood_batch, len_data=len(feature_matrix_train), batch_size=len(feature_matrix_train),
smoothing_window=1, label='batch, step_size=5e-1') | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Quiz Question: In the figure above, how many passes does batch gradient ascent need to achieve a similar log likelihood as stochastic gradient ascent?
It's always better
10 passes
20 passes
150 passes or more
Explore the effects of step sizes on stochastic gradient ascent
In previous sections, we chose step sizes fo... | batch_size = 100
num_passes = 10
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
coefficients_sgd = {}
log_likelihood_sgd = {}
for step_size in np.logspace(-4, 2, num=7):
coefficients_sgd[step_size], log_likelihood_sgd[step_size] = logistic_regression_SG(feature_matrix_train, sentiment_trai... | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Plotting the log likelihood as a function of passes for each step size
Now, we will plot the change in log likelihood using the make_plot for each of the following values of step_size:
step_size = 1e-4
step_size = 1e-3
step_size = 1e-2
step_size = 1e-1
step_size = 1e0
step_size = 1e1
step_size = 1e2
For consistency, ... | for step_size in np.logspace(-4, 2, num=7):
make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,
smoothing_window=30, label='step_size=%.1e'%step_size) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Now, let us remove the step size step_size = 1e2 and plot the rest of the curves. | for step_size in np.logspace(-4, 2, num=7)[0:6]:
make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,
smoothing_window=30, label='step_size=%.1e'%step_size) | machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb | tuanavu/coursera-university-of-washington | mit |
Hat potential
The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the "hat potential":
$$ V(x) = -a x^2 + b x^4 $$
Write a function hat(x,a,b) that returns the value of this function: | def hat(x,a,b):
v = -a*(x**2) + b*(x**4)
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0 | assignments/assignment11/OptimizationEx01.ipynb | jegibbs/phys202-2015-work | mit |
Plot this function over the range $x\in\left[-3,3\right]$ with $b=1.0$ and $a=5.0$: | a = 5.0
b = 1.0
x = np.linspace(-3.0, 3.0)
plt.plot(x, hat(x,a,b))
plt.plot(-1.5811388304396232, hat(-1.5811388304396232,a,b), 'ro')
plt.plot(1.58113882, hat(1.58113882,a,b), 'ro')
plt.xlabel('X')
plt.ylabel('V(x)')
plt.title('Hat Potential')
plt.grid(True)
plt.box(False);
assert True # leave this to grade the plot | assignments/assignment11/OptimizationEx01.ipynb | jegibbs/phys202-2015-work | mit |
Write code that finds the two local minima of this function for $b=1.0$ and $a=5.0$.
Use scipy.optimize.minimize to find the minima. You will have to think carefully about how to get this function to find both minima.
Print the x values of the minima.
Plot the function as a blue line.
On the same axes, show the minima... | opt.minimize(hat, -3, args=(a,b), method = "Powell")
opt.minimize(hat, -3, args=(a,b))
assert True # leave this for grading the plot | assignments/assignment11/OptimizationEx01.ipynb | jegibbs/phys202-2015-work | mit |
We start by generating some toy data containing 6 instances which we will partition into folds. | data = list(range(6))
labels = [True] * 3 + [False] * 3 | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Standard cross-validation <a id=standard></a>
Each function to be decorated with cross-validation functionality must accept the following arguments:
- x_train: training data
- x_test: test data
- y_train: training labels (required only when y is specified in the cross-validation decorator)
- y_test: test labels (requir... | def f(x_train, y_train, x_test, y_test):
print("")
print("train data:\t" + str(x_train) + "\t train labels:\t" + str(y_train))
print("test data:\t" + str(x_test) + "\t test labels:\t" + str(y_test))
return 0.0 | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
We start with 2 folds, which leads to equally sized train and test partitions. | f_2folds = optunity.cross_validated(x=data, y=labels, num_folds=2)(f)
print("using 2 folds")
f_2folds()
# f_2folds as defined above would typically be written using decorator syntax as follows
# we don't do that in these examples so we can reuse the toy objective function
@optunity.cross_validated(x=data, y=labels, n... | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
If we use three folds instead of 2, we get 3 iterations in which the training set is twice the size of the test set. | f_3folds = optunity.cross_validated(x=data, y=labels, num_folds=3)(f)
print("using 3 folds")
f_3folds() | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
If we do two iterations of 3-fold cross-validation (denoted by 2x3 fold), two sets of folds are generated and evaluated. | f_2x3folds = optunity.cross_validated(x=data, y=labels, num_folds=3, num_iter=2)(f)
print("using 2x3 folds")
f_2x3folds() | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Using strata and clusters<a id=strata-clusters></a>
Strata are defined as sets of instances that should be spread out across folds as much as possible (e.g. stratify patients by age). Clusters are sets of instances that must be put in a single fold (e.g. cluster measurements of the same patient).
Optunity allows you to... | strata = [[0, 1], [2, 3]]
f_stratified = optunity.cross_validated(x=data, y=labels, strata=strata, num_folds=3)(f)
f_stratified() | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Clusters
Clusters work similarly, except that now instances within a cluster are guaranteed to be placed within a single fold. The way to specify clusters is identical to strata. We create two clusters: ${0, 1}$ and ${2, 3}$. These pairs will always occur in a single fold. | clusters = [[0, 1], [2, 3]]
f_clustered = optunity.cross_validated(x=data, y=labels, clusters=clusters, num_folds=3)(f)
f_clustered() | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Strata and clusters
Strata and clusters can be used together. Lets say we have the following configuration:
1 stratum: ${0, 1, 2}$
2 clusters: ${0, 3}$, ${4, 5}$
In this particular example, instances 1 and 2 will inevitably end up in a single fold, even though they are part of one stratum. This happens because the to... | strata = [[0, 1, 2]]
clusters = [[0, 3], [4, 5]]
f_strata_clustered = optunity.cross_validated(x=data, y=labels, clusters=clusters, strata=strata, num_folds=3)(f)
f_strata_clustered() | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Aggregators <a id=aggregators></a>
Aggregators are used to combine the scores per fold into a single result. The default approach used in cross-validation is to take the mean of all scores. In some cases, we might be interested in worst-case or best-case performance, the spread, ...
Opunity allows passing a custom call... | @optunity.cross_validated(x=data, num_folds=3)
def f(x_train, x_test):
result = x_test[0]
print(result)
return result
f(1) | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
This can be replaced by any function, e.g. min or max. | @optunity.cross_validated(x=data, num_folds=3, aggregator=max)
def fmax(x_train, x_test):
result = x_test[0]
print(result)
return result
fmax(1)
@optunity.cross_validated(x=data, num_folds=3, aggregator=min)
def fmin(x_train, x_test):
result = x_test[0]
print(result)
return result
fmin(1) | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Retaining intermediate results
Often, it may be useful to retain all intermediate results, not just the final aggregated data. This is made possible via optunity.cross_validation.mean_and_list aggregator. This aggregator computes the mean for internal use in cross-validation, but also returns a list of lists containing... | @optunity.cross_validated(x=data, num_folds=3,
aggregator=optunity.cross_validation.mean_and_list)
def f_full(x_train, x_test, coeff):
return x_test[0] * coeff
# evaluate f
mean_score, all_scores = f_full(1.0)
print(mean_score)
print(all_scores)
| notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Note that a cross-validation based on the mean_and_list aggregator essentially returns a tuple of results. If the result is iterable, all solvers in Optunity use the first element as the objective function value. You can let the cross-validation procedure return other useful statistics too, which you can access from th... | opt_coeff, info, _ = optunity.minimize(f_full, coeff=[0, 1], num_evals=10)
print(opt_coeff)
print("call log")
for args, val in zip(info.call_log['args']['coeff'], info.call_log['values']):
print(str(args) + '\t\t' + str(val)) | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Cross-validation with scikit-learn <a id=cv-sklearn></a>
In this example we will show how to use cross-validation methods that are provided by scikit-learn in conjunction with Optunity. To do this we provide Optunity with the folds that scikit-learn produces in a specific format.
In supervised learning datasets often... | data = list(range(20))
labels = [1 if i%4==0 else 0 for i in range(20)]
@optunity.cross_validated(x=data, y=labels, num_folds=5)
def unbalanced_folds(x_train, y_train, x_test, y_test):
print("")
print("train data:\t" + str(x_train) + "\ntrain labels:\t" + str(y_train)) + '\n'
print("test data:\t" + str(x_t... | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Notice above how the test label sets have a varying number of postive samples, some have none, some have one, and some have two. | from sklearn.cross_validation import StratifiedKFold
stratified_5folds = StratifiedKFold(labels, n_folds=5)
folds = [[list(test) for train, test in stratified_5folds]]
@optunity.cross_validated(x=data, y=labels, folds=folds, num_folds=5)
def balanced_folds(x_train, y_train, x_test, y_test):
print("")
print("t... | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
Now all of our train sets have four positive samples and our test sets have one positive sample.
To use predetermined folds, place a list of the test sample idices into a list. And then insert that list into another list. Why so many nested lists? Because you can perform multiple cross-validation runs by setting num_it... | data = list(range(6))
labels = [True] * 3 + [False] * 3
fold1 = [[0, 3], [1, 4], [2, 5]]
fold2 = [[0, 5], [1, 4], [0, 3]] # notice what happens when the indices are not unique
folds = [fold1, fold2]
@optunity.cross_validated(x=data, y=labels, folds=folds, num_folds=3, num_iter=2)
def multiple_iters(x_train, y_train, ... | notebooks/basic-cross-validation.ipynb | chrinide/optunity | bsd-3-clause |
The one-step lookahead agent is defined in the next code cell. | # The agent is always implemented as a Python function that accepts two arguments: obs and config
def agent(obs, config):
# Get list of valid moves
valid_moves = [c for c in range(config.columns) if obs.board[c] == 0]
# Convert the board to a 2D grid
grid = np.asarray(obs.board).reshape(config.rows, con... | notebooks/game_ai/raw/tut2.ipynb | Kaggle/learntools | apache-2.0 |
In the code for the agent, we begin by getting a list of valid moves. This is the same line of code we used in the previous tutorial!
Next, we convert the game board to a 2D numpy array. For Connect Four, grid is an array with 6 rows and 7 columns.
Then, the score_move() function calculates the value of the heuristic... | from kaggle_environments import make, evaluate
# Create the game environment
env = make("connectx")
# Two random agents play one game round
env.run([agent, "random"])
# Show the game
env.render(mode="ipython") | notebooks/game_ai/raw/tut2.ipynb | Kaggle/learntools | apache-2.0 |
We use the get_win_percentage() function from the previous tutorial to check how we can expect it to perform on average. | #$HIDE_INPUT$
def get_win_percentages(agent1, agent2, n_rounds=100):
# Use default Connect Four setup
config = {'rows': 6, 'columns': 7, 'inarow': 4}
# Agent 1 goes first (roughly) half the time
outcomes = evaluate("connectx", [agent1, agent2], config, [], n_rounds//2)
# Agent 2 goes first... | notebooks/game_ai/raw/tut2.ipynb | Kaggle/learntools | apache-2.0 |
Camera Calibration with OpenCV
Run the code in the cell below to extract object points and image points for camera calibration. | import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
%matplotlib qt
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*8,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpo... | CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb | charliememory/AutonomousDriving | gpl-3.0 |
If the above cell ran sucessfully, you should now have objpoints and imgpoints needed for camera calibration. Run the cell below to calibrate, calculate distortion coefficients, and test undistortion on an image! | import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('calibration_wide/test_image.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst =... | CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb | charliememory/AutonomousDriving | gpl-3.0 |
Now we will form the request to invoke the Open Street Map API. Documentation on this API is found here:
http://wiki.openstreetmap.org/wiki/Nominatim
First, we'll generate an example address to geocode. Why not use Environment Hall? But feel free to use your own address! | #Get the address
address = '9 Circuit Drive, Durham, NC, 27708' | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
An API request consists two components: the service endpoint and a set of parameters associated with the service.
When using the requests module to create and send our request, we supply the service endpoint is a string containing the server address (as a URL) and the service name (here, it's search). And the paramete... | #Form the request
osmURL = 'http://nominatim.openstreetmap.org/search'
params = {'format':'json','q':address} | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
Now, we can use requests to send our command off to the OSM server. The server's response is saved as the response variable. | #Send the request
response = requests.get(osmURL, params) | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
The response object below contains a lot of information. You are encouraged to explore this object further. Here we'll explore one property which is the full URL created. Copy and paste the result in your favorite browser, and you'll see the result of our request in raw form. When you try this, try changing 'json' to '... | response.url
#Opens the URL as an html response (vs JSON) in a web browser...
import webbrowser
webbrowser.open_new(response.url.replace('json','html')) | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
What we really want from the response, however, is the data returned by the service. The json function of the response object converts the response to an object in JavaScript Object Notation, or JSON. JSON is esentially a list of dictionaries that we can easily manipulate in Python. | #Read in the response as a JSON encoded object
jsonObj = response.json() | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
pprint or "pretty print" allows us to display JSON objects in a readable format. Let's make a pretty print of our JSON repsonse. | from pprint import pprint
pprint(jsonObj) | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
Our response contains only one item in the JSON list. We'll extract to a dictionary and print it's items. | dataDict = jsonObj[0]
print dataDict.keys() | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
Now we can easily grab the lat and lon objects from our response | lat = float(dataDict['lat'])
lng = float(dataDict['lon'])
print "The lat,lng
d = jsonObj[0]
d['lon'],d['lat'] | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
Now let's inform the user of the result of the whole process... | print "The address {0} is located at\n{1}° Lat, {2}° Lon".format(address,lat,lng) | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | johnpfay/environ859 | gpl-3.0 |
Make the notebook reproducible | np.random.seed(3123) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Nested analysis
In our discussion below, "Group 2" is nested within "Group 1". As a
concrete example, "Group 1" might be school districts, with "Group
2" being individual schools. The function below generates data from
such a population. In a nested analysis, the group 2 labels that
are nested within different group... | def generate_nested(
n_group1=200, n_group2=20, n_rep=10, group1_sd=2, group2_sd=3, unexplained_sd=4
):
# Group 1 indicators
group1 = np.kron(np.arange(n_group1), np.ones(n_group2 * n_rep))
# Group 1 effects
u = group1_sd * np.random.normal(size=n_group1)
effects1 = np.kron(u, np.ones(n_group2... | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Generate a data set to analyze. | df = generate_nested() | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Using all the default arguments for generate_nested, the population
values of "group 1 Var" and "group 2 Var" are 2^2=4 and 3^2=9,
respectively. The unexplained variance, listed as "scale" at the
top of the summary table, has population value 4^2=16. | model1 = sm.MixedLM.from_formula(
"y ~ 1",
re_formula="1",
vc_formula={"group2": "0 + C(group2)"},
groups="group1",
data=df,
)
result1 = model1.fit()
print(result1.summary()) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
If we wish to avoid the formula interface, we can fit the same model
by building the design matrices manually. | def f(x):
n = x.shape[0]
g2 = x.group2
u = g2.unique()
u.sort()
uv = {v: k for k, v in enumerate(u)}
mat = np.zeros((n, len(u)))
for i in range(n):
mat[i, uv[g2.iloc[i]]] = 1
colnames = ["%d" % z for z in u]
return mat, colnames | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Then we set up the variance components using the VCSpec class. | vcm = df.groupby("group1").apply(f).to_list()
mats = [x[0] for x in vcm]
colnames = [x[1] for x in vcm]
names = ["group2"]
vcs = VCSpec(names, [colnames], [mats]) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Finally we fit the model. It can be seen that the results of the
two fits are identical. | oo = np.ones(df.shape[0])
model2 = sm.MixedLM(df.y, oo, exog_re=oo, groups=df.group1, exog_vc=vcs)
result2 = model2.fit()
print(result2.summary()) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Crossed analysis
In a crossed analysis, the levels of one group can occur in any
combination with the levels of the another group. The groups in
Statsmodels MixedLM are always nested, but it is possible to fit a
crossed model by having only one group, and specifying all random
effects as variance components. Many, bu... | def generate_crossed(
n_group1=100, n_group2=100, n_rep=4, group1_sd=2, group2_sd=3, unexplained_sd=4
):
# Group 1 indicators
group1 = np.kron(
np.arange(n_group1, dtype=int), np.ones(n_group2 * n_rep, dtype=int)
)
group1 = group1[np.random.permutation(len(group1))]
# Group 1 effects
... | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Generate a data set to analyze. | df = generate_crossed() | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Next we fit the model, note that the groups vector is constant.
Using the default parameters for generate_crossed, the level 1
variance should be 2^2=4, the level 2 variance should be 3^2=9, and
the unexplained variance should be 4^2=16. | vc = {"g1": "0 + C(group1)", "g2": "0 + C(group2)"}
oo = np.ones(df.shape[0])
model3 = sm.MixedLM.from_formula("y ~ 1", groups=oo, vc_formula=vc, data=df)
result3 = model3.fit()
print(result3.summary()) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
If we wish to avoid the formula interface, we can fit the same model
by building the design matrices manually. | def f(g):
n = len(g)
u = g.unique()
u.sort()
uv = {v: k for k, v in enumerate(u)}
mat = np.zeros((n, len(u)))
for i in range(n):
mat[i, uv[g[i]]] = 1
colnames = ["%d" % z for z in u]
return [mat], [colnames]
vcm = [f(df.group1), f(df.group2)]
mats = [x[0] for x in vcm]
colnames... | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
Here we fit the model without using formulas, it is simple to check
that the results for models 3 and 4 are identical. | oo = np.ones(df.shape[0])
model4 = sm.MixedLM(df.y, oo[:, None], exog_re=None, groups=oo, exog_vc=vcs)
result4 = model4.fit()
print(result4.summary()) | v0.13.0/examples/notebooks/generated/variance_components.ipynb | statsmodels/statsmodels.github.io | bsd-3-clause |
We can access individual rows and columns using .loc (with index labels) or .iloc (with indices)
python
medians_df.loc[row labels, column labels]
medians_df.iloc[row indices, column indices] | medians_df.loc[[0, 1, 2, 5], 'County']
medians_df.iloc[10:15, :4] | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
We can also get just a few columns from all rows | medians_df[['Median_age', 'Avg_MonthlyIncome']].head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Extending pandas Spatially
The ArcGIS API for Python provides Spatially Enabled DataFrames, which include geometry information. | from arcgis.features import GeoAccessor, GeoSeriesAccessor
counties_fc_path = r'C:\Users\jdadams\AppData\Roaming\Esri\ArcGISPro\Favorites\opensgid.agrc.utah.gov.sde\opensgid.boundaries.county_boundaries'
counties_df = pd.DataFrame.spatial.from_featureclass(counties_fc_path)
counties_df.head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
pandas lets you work on rows that meet a certain condition | counties_df.loc[counties_df['stateplane'] == 'Central', ['name', 'stateplane', 'fips_str']] | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
You can easily add new columns | counties_df['emperor'] = 'Jake'
counties_df.head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
pandas provides powerful built in grouping and aggregation tools, along with Spatially Enabled DataFrames' geometry operations | counties_df.groupby('stateplane').count()
counties_df['acres'] = counties_df['SHAPE'].apply(lambda shape: shape.area / 4046.8564)
counties_df.groupby('stateplane')['acres'].sum() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
pandas Solutions to our Arcpy Problems
row[0] Solution: Field Names
```python
def update_unit_count(parcels_df):
"""Update unit counts in-place for single family, duplex, and tri/quad
Args:
parcels_df (pd.DataFrame): The evaluated parcel dataset with UNIT_COUNT, HOUSE_CNT, SUBTYPE, and NOTE columns
"""
# fix ... | counties_df.loc[(counties_df['pop_lastcensus'] < 100000) & (counties_df['stateplane'] == 'North'), 'emperor'] = 'Erik'
counties_df[['name', 'pop_lastcensus', 'stateplane', 'emperor']].sort_values('name').head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Nested Cursors Solution: Merged DataFrames
```python
def _get_current_attachment_info_by_oid(self, live_data_subset_df):
#: Join live attachment table to feature layer info
live_attachments_df = pd.DataFrame(self.feature_layer.attachments.search())
live_attachments_subset_df = live_attachments_df.reindex(columns=['PARE... | census_fc_path = r'C:\Users\jdadams\AppData\Roaming\Esri\ArcGISPro\Favorites\opensgid.agrc.utah.gov.sde\opensgid.demographic.census_counties_2020'
census_df = pd.DataFrame.spatial.from_featureclass(census_fc_path)
counties_with_census_df = counties_df.merge(census_df[['geoid20', 'aland20']], left_on='fips_str', right_o... | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Renaming/Reordering Fields Solution: df.rename() and df.reindex()
python
final_parcels_df.rename(
columns={
'name': 'CITY', #: from cities
'NewSA': 'SUBCOUNTY', #: From subcounties/regions
'BUILT_YR': 'APX_BLT_YR',
'BLDG_SQFT': 'TOT_BD_FT2',
'TOTAL_MKT_VALUE': 'TOT_VALUE',
... | renames = {
'name': 'County Name',
'pop_lastcensus': 'Last Census Population',
'emperor': 'Benevolent Dictator for Life',
'acres': 'Acres',
'aland20': 'Land Area',
}
counties_with_census_df.rename(columns=renames, inplace=True)
counties_with_census_df.head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Now that we've got it all looking good, let's reorder the fields and get rid of the ones we don't want | field_order = [
'County Name',
'Benevolent Dictator for Life',
'Acres',
'Land Area',
'Last Census Population',
'SHAPE'
]
final_counties_df = counties_with_census_df.reindex(columns=field_order)
final_counties_df.head() | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Intermediate Feature Classes: New DataFrame Variables
With everything we've done, we've not written a single feature class to either disk or in_memory
python
counties_df
counties_with_census_df
final_counties_df
Finally, Write It All To Disk | final_counties_df.spatial.to_featureclass(r'C:\gis\Projects\HousingInventory\HousingInventory.gdb\counties_ugic') | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | agrc/Presentations | mit |
Overview
Loading the extension enables three magic functions: %octave, %octave_push, and %octave_pull.
The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.
Here you see an example of how to execute a single line of Octave, and how to tr... | x = %octave [1 2; 3 4];
x
a = [1, 2, 3]
%octave_push a
%octave a = a * 2;
%octave_pull a
a | example/octavemagic_extension.ipynb | blink1073/oct2py | mit |
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