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Choosing the right model and learning algorithm | # creating a error calc fuction
def error(f, x, y):
return np.sum((f(x) - y)**2) | BMLSwPython/01_GettingStarted_withPython.ipynb | atulsingh0/MachineLearning | gpl-3.0 |
Linear 1-d model | # sp's polyfit func do the same
fp1, residuals, rank, sv, rcond = sp.polyfit(X, y, 1, full=True)
print(fp1)
print(residuals)
# generating the one order function
f1 = sp.poly1d(fp1)
# checking error
print("Error : ",error(f1, X, y))
x1 = np.array([-100, np.max(X)+100])
y1 = f1(x1)
ax.plot(x1, y1, c='g', linewidth=2... | BMLSwPython/01_GettingStarted_withPython.ipynb | atulsingh0/MachineLearning | gpl-3.0 |
$$ f(x) = 2.59619213 * x + 989.02487106 $$
Polynomial 2-d | # sp's polyfit func do the same
fp2 = sp.polyfit(X, y, 2)
print(fp2)
# generating the 2 order function
f2= sp.poly1d(fp2)
# checking error
print("Error : ",error(f2, X, y))
x1= np.linspace(-100, np.max(X)+100, 2000)
y2= f2(x1)
ax.plot(x1, y2, c='r', linewidth=2)
ax.legend(["data", "d = %i" % f1.order, "d = %i" % f... | BMLSwPython/01_GettingStarted_withPython.ipynb | atulsingh0/MachineLearning | gpl-3.0 |
$$ f(x) = 0.0105322215 * x^2 - 5.26545650 * x + 1974.6082 $$
What if we want to regress two response output instead of one, As we can see in the graph that there is a steep change in data between week 3 and 4, so let's draw two reponses line, one for the data between week0 and week3.5 and second for week3.5 to week5 | # we are going to divide the data on time so
div = 3.5*7*24
X1 = X[X<=div]
Y1 = y[X<=div]
X2 = X[X>div]
Y2 = y[X>div]
# now plotting the both data
fa = sp.poly1d(sp.polyfit(X1, Y1, 1))
fb = sp.poly1d(sp.polyfit(X2, Y2, 1))
fa_error = error(fa, X1, Y1)
fb_error = error(fb, X2, Y2)
print("Error inflection = %f" % (... | BMLSwPython/01_GettingStarted_withPython.ipynb | atulsingh0/MachineLearning | gpl-3.0 |
Suppose we choose that function with degree 2 is best fit for our data and want to predict that if everything will go same then when we will hit the 100000 count ??
$$ 0 = f(x) - 100000 = 0.0105322215 * x^2 - 5.26545650 * x + 1974.6082 - 100000 $$
SciPy's optimize module has the function
fsolve that achieves this, ... | print(f2)
print(f2 - 100000)
# import
from scipy.optimize import fsolve
reached_max = fsolve(f2-100000, x0=800)/(7*24)
print("100,000 hits/hour expected at week %f" % reached_max[0]) | BMLSwPython/01_GettingStarted_withPython.ipynb | atulsingh0/MachineLearning | gpl-3.0 |
datacleaning
The datacleaning module is used to clean and organize the data into 51 CSV files corresponding to the 50 states of the US and the District of Columbia.
The wrapping function clean_all_data takes all the data sets as input and sorts the data in to CSV files of the states.
The CSVs are stored in the Cleane... | data_cleaning.clean_all_data() | examples/Demo.ipynb | uwkejia/Clean-Energy-Outlook | mit |
missing_data
The missing_data module is used to estimate the missing data of the GDP (from 1960 - 1962) and determine the values of the predictors (from 2016-2020).
The wrapping function predict_all takes the CSV files of the states as input and stores the predicted missing values in the same CSV files.
The CSVs gener... | missing_data.predict_all() | examples/Demo.ipynb | uwkejia/Clean-Energy-Outlook | mit |
ridge_prediction
The ridge_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using ridge regression.
The wrapping function ridge_predict_all takes the CSV files of the states as input and stores the future values of the ene... | ridge_prediction.ridge_predict_all() | examples/Demo.ipynb | uwkejia/Clean-Energy-Outlook | mit |
svr_prediction
The svr_prediction module is used to predict the future values of energies like wind energy, solar energy, hydro energy and nuclear energy from 2016-2020 using Support Vector Regression
The wrapping function SVR_predict_all takes the CSV files of the states as input and stores the future values of the e... | svr_prediction.SVR_predict_all() | examples/Demo.ipynb | uwkejia/Clean-Energy-Outlook | mit |
plots
Visualizations is done using Tableau software. The Tableau workbook for the predicted data is included in the repository. The Tableau dashboard created for this data is illustrated below: | %%HTML
<div class='tableauPlaceholder' id='viz1489609724011' style='position: relative'><noscript><a href='#'><img alt='Clean Energy Production in the contiguous United States(in million kWh) ' src='https://public.tableau.com/static/images/PB/PB87S38NW/1_rss.png' style='border: none' /></a>... | examples/Demo.ipynb | uwkejia/Clean-Energy-Outlook | mit |
Visualize source leakage among labels using a circular graph
This example computes all-to-all pairwise leakage among 68 regions in
source space based on MNE inverse solutions and a FreeSurfer cortical
parcellation. Label-to-label leakage is estimated as the correlation among the
labels' point-spread functions (PSFs). I... | # Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery)
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot a... | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Load forward solution and inverse operator
We need a matching forward solution and inverse operator to compute
resolution matrices for different methods. | data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-fixed-inv.fif'
forward = mne.read_forward_solution(fname_fwd)
# Convert forward solution to fixed source orient... | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Read and organise labels for cortical parcellation
Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi | labels = mne.read_labels_from_annot('sample', parc='aparc',
subjects_dir=subjects_dir)
n_labels = len(labels)
label_colors = [label.color for label in labels]
# First, we reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
lh_lab... | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Compute point-spread function summaries (PCA) for all labels
We summarise the PSFs per label by their first five principal components, and
use the first component to evaluate label-to-label leakage below. | # Compute first PCA component across PSFs within labels.
# Note the differences in explained variance, probably due to different
# spatial extents of labels.
n_comp = 5
stcs_psf_mne, pca_vars_mne = get_point_spread(
rm_mne, src, labels, mode='pca', n_comp=n_comp, norm=None,
return_pca_vars=True)
n_verts = rm_mn... | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
We can show the explained variances of principal components per label. Note
how they differ across labels, most likely due to their varying spatial
extent. | with np.printoptions(precision=1):
for [name, var] in zip(label_names, pca_vars_mne):
print(f'{name}: {var.sum():.1f}% {var}') | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
The output shows the summed variance explained by the first five principal
components as well as the explained variances of the individual components.
Evaluate leakage based on label-to-label PSF correlations
Note that correlations ignore the overall amplitude of PSFs, i.e. they do
not show which region will potentiall... | # get PSFs from Source Estimate objects into matrix
psfs_mat = np.zeros([n_labels, n_verts])
# Leakage matrix for MNE, get first principal component per label
for [i, s] in enumerate(stcs_psf_mne):
psfs_mat[i, :] = s.data[:, 0]
# Compute label-to-label leakage as Pearson correlation of PSFs
# Sign of correlation is... | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Most leakage occurs for neighbouring regions, but also for deeper regions
across hemispheres.
Save the figure (optional)
Matplotlib controls figure facecolor separately for interactive display
versus for saved figures. Thus when saving you must specify facecolor,
else your labels, title, etc will not be visible::
>&... | # left and right lateral occipital
idx = [22, 23]
stc_lh = stcs_psf_mne[idx[0]]
stc_rh = stcs_psf_mne[idx[1]]
# Maximum for scaling across plots
max_val = np.max([stc_lh.data, stc_rh.data]) | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
Point-spread function for the lateral occipital label in the left hemisphere | brain_lh = stc_lh.plot(subjects_dir=subjects_dir, subject='sample',
hemi='both', views='caudal',
clim=dict(kind='value',
pos_lims=(0, max_val / 2., max_val)))
brain_lh.add_text(0.1, 0.9, label_names[idx[0]], 'title', font_size=16) | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
and in the right hemisphere. | brain_rh = stc_rh.plot(subjects_dir=subjects_dir, subject='sample',
hemi='both', views='caudal',
clim=dict(kind='value',
pos_lims=(0, max_val / 2., max_val)))
brain_rh.add_text(0.1, 0.9, label_names[idx[1]], 'title', font_size=16) | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | mne-tools/mne-tools.github.io | bsd-3-clause |
DTensor Concepts
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/guide/dtensor_overview"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="https://colab.research.google.c... | !pip install --quiet --upgrade --pre tensorflow | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Once installed, import tensorflow and tf.experimental.dtensor. Then configure TensorFlow to use 6 virtual CPUs.
Even though this example uses vCPUs, DTensor works the same way on CPU, GPU or TPU devices. | import tensorflow as tf
from tensorflow.experimental import dtensor
print('TensorFlow version:', tf.__version__)
def configure_virtual_cpus(ncpu):
phy_devices = tf.config.list_physical_devices('CPU')
tf.config.set_logical_device_configuration(phy_devices[0], [
tf.config.LogicalDeviceConfiguration(),
]... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
DTensor's model of distributed tensors
DTensor introduces two concepts: dtensor.Mesh and dtensor.Layout. They are abstractions to model the sharding of tensors across topologically related devices.
Mesh defines the device list for computation.
Layout defines how to shard the Tensor dimension on a Mesh.
Mesh
Mesh repr... | mesh_1d = dtensor.create_mesh([('x', 6)], devices=DEVICES)
print(mesh_1d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
A Mesh can be multi dimensional as well. In the following example, 6 CPU devices form a 3x2 mesh, where the 'x' mesh dimension has a size of 3 devices, and the 'y' mesh dimension has a size of 2 devices:
<img src="https://www.tensorflow.org/images/dtensor/dtensor_mesh_2d.png" alt="A 2 dimensional mesh with 6 CPUs"
... | mesh_2d = dtensor.create_mesh([('x', 3), ('y', 2)], devices=DEVICES)
print(mesh_2d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Layout
Layout specifies how a tensor is distributed, or sharded, on a Mesh.
Note: In order to avoid confusions between Mesh and Layout, the term dimension is always associated with Mesh, and the term axis with Tensor and Layout in this guide.
The rank of Layout should be the same as the rank of the Tensor where the Lay... | layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh_1d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Using the same tensor and mesh the layout Layout(['unsharded', 'x']) would shard the second axis of the tensor across the 6 devices.
<img src="https://www.tensorflow.org/images/dtensor/dtensor_layout_rank1.png" alt="A tensor sharded across a rank-1 mesh" class="no-filter"> | layout = dtensor.Layout([dtensor.UNSHARDED, 'x'], mesh_1d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Given a 2-dimensional 3x2 mesh such as [("x", 3), ("y", 2)], (mesh_2d from the previous section), Layout(["y", "x"], mesh_2d) is a layout for a rank-2 Tensor whose first axis is sharded across across mesh dimension "y", and whose second axis is sharded across mesh dimension "x".
<img src="https://www.tensorflow.org/ima... | layout = dtensor.Layout(['y', 'x'], mesh_2d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
For the same mesh_2d, the layout Layout(["x", dtensor.UNSHARDED], mesh_2d) is a layout for a rank-2 Tensor that is replicated across "y", and whose first axis is sharded on mesh dimension x.
<img src="https://www.tensorflow.org/images/dtensor/dtensor_layout_hybrid.png" alt="A tensor replicated across mesh-dimension y, ... | layout = dtensor.Layout(["x", dtensor.UNSHARDED], mesh_2d) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Single-Client and Multi-Client Applications
DTensor supports both single-client and multi-client applications. The colab Python kernel is an example of a single client DTensor application, where there is a single Python process.
In a multi-client DTensor application, multiple Python processes collectively perform as a ... | def dtensor_from_array(arr, layout, shape=None, dtype=None):
"""Convert a DTensor from something that looks like an array or Tensor.
This function is convenient for quick doodling DTensors from a known,
unsharded data object in a single-client environment. This is not the
most efficient way of creating a DTens... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Anatomy of a DTensor
A DTensor is a tf.Tensor object, but augumented with the Layout annotation that defines its sharding behavior. A DTensor consists of the following:
Global tensor meta-data, including the global shape and dtype of the tensor.
A Layout, which defines the Mesh the Tensor belongs to, and how the Tenso... | mesh = dtensor.create_mesh([("x", 6)], devices=DEVICES)
layout = dtensor.Layout([dtensor.UNSHARDED], mesh)
my_first_dtensor = dtensor_from_array([0, 1], layout)
# Examine the dtensor content
print(my_first_dtensor)
print("global shape:", my_first_dtensor.shape)
print("dtype:", my_first_dtensor.dtype) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Layout and fetch_layout
The layout of a DTensor is not a regular attribute of tf.Tensor. Instead, DTensor provides a function, dtensor.fetch_layout to access the layout of a DTensor. | print(dtensor.fetch_layout(my_first_dtensor))
assert layout == dtensor.fetch_layout(my_first_dtensor) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Component tensors, pack and unpack
A DTensor consists of a list of component tensors. The component tensor for a device in the Mesh is the Tensor object representing the piece of the global DTensor that is stored on this device.
A DTensor can be unpacked into component tensors through dtensor.unpack. You can make use o... | for component_tensor in dtensor.unpack(my_first_dtensor):
print("Device:", component_tensor.device, ",", component_tensor) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
As shown, my_first_dtensor is a tensor of [0, 1] replicated to all 6 devices.
The inverse operation of dtensor.unpack is dtensor.pack. Component tensors can be packed back into a DTensor.
The components must have the same rank and dtype, which will be the rank and dtype of the returned DTensor. However there is no stri... | packed_dtensor = dtensor.pack(
[[0, 1], [0, 1], [0, 1],
[0, 1], [0, 1], [0, 1]],
layout=layout
)
print(packed_dtensor) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Sharding a DTensor to a Mesh
So far you've worked with the my_first_dtensor, which is a rank-1 DTensor fully replicated across a dim-1 Mesh.
Next create and inspect DTensors that are sharded across a dim-2 Mesh. The next example does this with a 3x2 Mesh on 6 CPU devices, where size of mesh dimension 'x' is 3 devices, ... | mesh = dtensor.create_mesh([("x", 3), ("y", 2)], devices=DEVICES) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Fully sharded rank-2 Tensor on a dim-2 Mesh
Create a 3x2 rank-2 DTensor, sharding its first axis along the 'x' mesh dimension, and its second axis along the 'y' mesh dimension.
Because the tensor shape equals to the mesh dimension along all of the sharded axes, each device receives a single element of the DTensor.
The... | fully_sharded_dtensor = dtensor_from_array(
tf.reshape(tf.range(6), (3, 2)),
layout=dtensor.Layout(["x", "y"], mesh))
for raw_component in dtensor.unpack(fully_sharded_dtensor):
print("Device:", raw_component.device, ",", raw_component) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Fully replicated rank-2 Tensor on a dim-2 Mesh
For comparison, create a 3x2 rank-2 DTensor, fully replicated to the same dim-2 Mesh.
Because the DTensor is fully replicated, each device receives a full replica of the 3x2 DTensor.
The rank of the component tensors are the same as the rank of the global shape -- this fa... | fully_replicated_dtensor = dtensor_from_array(
tf.reshape(tf.range(6), (3, 2)),
layout=dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh))
# Or, layout=tensor.Layout.fully_replicated(mesh, rank=2)
for component_tensor in dtensor.unpack(fully_replicated_dtensor):
print("Device:", component_tensor.de... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Hybrid rank-2 Tensor on a dim-2 Mesh
What about somewhere between fully sharded and fully replicated?
DTensor allows a Layout to be a hybrid, sharded along some axes, but replicated along others.
For example, you can shard the same 3x2 rank-2 DTensor in the following way:
1st axis sharded along the 'x' mesh dimension.... | hybrid_sharded_dtensor = dtensor_from_array(
tf.reshape(tf.range(6), (3, 2)),
layout=dtensor.Layout(['x', dtensor.UNSHARDED], mesh))
for component_tensor in dtensor.unpack(hybrid_sharded_dtensor):
print("Device:", component_tensor.device, ",", component_tensor) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
You can inspect the component tensors of the created DTensor and verify they are indeed sharded according to your scheme. It may be helpful to illustrate the situation with a chart:
<img src="https://www.tensorflow.org/images/dtensor/dtensor_hybrid_mesh.png" alt="A 3x2 hybrid mesh with 6 CPUs"
class="no-filter" wi... | print(fully_replicated_dtensor.numpy())
try:
fully_sharded_dtensor.numpy()
except tf.errors.UnimplementedError:
print("got an error as expected for fully_sharded_dtensor")
try:
hybrid_sharded_dtensor.numpy()
except tf.errors.UnimplementedError:
print("got an error as expected for hybrid_sharded_dtensor") | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
TensorFlow API on DTensor
DTensor strives to be a drop-in replacement for tensor in your program. The TensorFlow Python API that consume tf.Tensor, such as the Ops library functions, tf.function, tf.GradientTape, also work with DTensor.
To accomplish this, for each TensorFlow Graph, DTensor produces and executes an equ... | mesh = dtensor.create_mesh([("x", 6)], devices=DEVICES)
layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)
a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=layout)
b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=layout)
c = tf.matmul(a, b) # runs 6 identical matmuls in parallel on 6 dev... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Sharding operands along the contracted axis
You can reduce the amount of computation per device by sharding the operands a and b. A popular sharding scheme for tf.matmul is to shard the operands along the axis of the contraction, which means sharding a along the second axis, and b along the first axis.
The global matri... | mesh = dtensor.create_mesh([("x", 3), ("y", 2)], devices=DEVICES)
a_layout = dtensor.Layout([dtensor.UNSHARDED, 'x'], mesh)
a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=a_layout)
b_layout = dtensor.Layout(['x', dtensor.UNSHARDED], mesh)
b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=b_layout)
c = tf.... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Additional Sharding
You can perform additional sharding on the inputs, and they are appropriately carried over to the results. For example, you can apply additional sharding of operand a along its first axis to the 'y' mesh dimension. The additional sharding will be carried over to the first axis of the result c.
Total... | mesh = dtensor.create_mesh([("x", 3), ("y", 2)], devices=DEVICES)
a_layout = dtensor.Layout(['y', 'x'], mesh)
a = dtensor_from_array([[1, 2, 3], [4, 5, 6]], layout=a_layout)
b_layout = dtensor.Layout(['x', dtensor.UNSHARDED], mesh)
b = dtensor_from_array([[6, 5], [4, 3], [2, 1]], layout=b_layout)
c = tf.matmul(a, b)
... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
DTensor as Output
What about Python functions that do not take operands, but returns a Tensor result that can be sharded? Examples of such functions are
tf.ones, tf.zeros, tf.random.stateless_normal,
For these Python functions, DTensor provides dtensor.call_with_layout which eagelry executes a Python function with DT... | help(dtensor.call_with_layout) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
The eagerly executed Python function usually only contain a single non-trivial TensorFlow Op.
To use a Python function that emits multiple TensorFlow Ops with dtensor.call_with_layout, the function should be converted to a tf.function. Calling a tf.function is a single TensorFlow Op. When the tf.function is called, DTe... | help(tf.ones)
mesh = dtensor.create_mesh([("x", 3), ("y", 2)], devices=DEVICES)
ones = dtensor.call_with_layout(tf.ones, dtensor.Layout(['x', 'y'], mesh), shape=(6, 4))
print(ones) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
APIs that emit multiple TensorFlow Ops
If the API emits multiple TensorFlow Ops, convert the function into a single Op through tf.function. For example tf.random.stateleess_normal | help(tf.random.stateless_normal)
ones = dtensor.call_with_layout(
tf.function(tf.random.stateless_normal),
dtensor.Layout(['x', 'y'], mesh),
shape=(6, 4),
seed=(1, 1))
print(ones) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Wrapping a Python function that emits a single TensorFlow Op with tf.function is allowed. The only caveat is paying the associated cost and complexity of creating a tf.function from a Python function. | ones = dtensor.call_with_layout(
tf.function(tf.ones),
dtensor.Layout(['x', 'y'], mesh),
shape=(6, 4))
print(ones) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
From tf.Variable to dtensor.DVariable
In Tensorflow, tf.Variable is the holder for a mutable Tensor value.
With DTensor, the corresponding variable semantics is provided by dtensor.DVariable.
The reason a new type DVariable was introduced for DTensor variable is because DVariables have an additional requirement that th... | mesh = dtensor.create_mesh([("x", 6)], devices=DEVICES)
layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)
v = dtensor.DVariable(
initial_value=dtensor.call_with_layout(
tf.function(tf.random.stateless_normal),
layout=layout,
shape=tf.TensorShape([64, 32]),
seed=[... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Other than the requirement on matching the layout, a DVariable behaves the same as a tf.Variable. For example, you can add a DVariable to a DTensor, | a = dtensor.call_with_layout(tf.ones, layout=layout, shape=(64, 32))
b = v + a # add DVariable and DTensor
print(b) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
You can also assign a DTensor to a DVariable. | v.assign(a) # assign a DTensor to a DVariable
print(a) | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Attempting to mutate the layout of a DVariable, by assigning a DTensor with an incompatible layout produces an error. | # variable's layout is immutable.
another_mesh = dtensor.create_mesh([("x", 3), ("y", 2)], devices=DEVICES)
b = dtensor.call_with_layout(tf.ones,
layout=dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], another_mesh),
shape=(64, 32))
try:
v.assign(b)
except:
print("exc... | site/en/guide/dtensor_overview.ipynb | tensorflow/docs | apache-2.0 |
Reading TSV files | CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \
'guillaume_huguet_CNV','File_OK')
filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'
fullfname = osp.join(CWD, filename)
arr = np.loadtxt(fullfname, dtype='str', comments=None, delimiter='\Tab',
conv... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
transforming the "Pvalue_MMAP_V2_..." into danger score
Testing the function danger_score | assert util._test_danger_score_1()
assert util._test_danger_score() | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
QUESTION pour Guillaume:
a quoi correspondent les '' dans la colonne "Pvalue_MMAP_V2_sans_intron_and_Intergenic" (danger)?
Ansewer: cnv for which we have no dangerosity information | """
danger_not_empty = dangers != ''
danger_scores = dangers[danger_not_empty]
danger_scores = np.asarray([util.danger_score(pstr, util.pH1_with_apriori)
for pstr in danger_scores])
"""; | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
To be or not to be a CNV: p value from the 'SCORE' column | reload(util)
#get the scores
scores = np.asarray([line.split('\t')[i_score] for line in arr[1:]])
assert len(scores) == expected_nb_values
print(len(np.unique(scores)))
#tmp_score = np.asarray([util.str2floats(s, comma2point=True, sep=' ')[0] for s in scores])
assert scores.shape[0] == EXPECTED_LINES - 1
# h = plt.h... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Replace the zero score by the maximum score: cf Guillaume's procedure | scoresf = np.asarray([util.str2floats(s, comma2point=True, sep=' ')[0]
for s in scores])
print(scoresf.max(), scoresf.min(),(scoresf==0).sum())
#clean_score = util.process_scores(scores)
#h = plt.hist(clean_score[clean_score < 60], bins=100)
#h = plt.hist(sc... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Transforms the scores into P(cnv is real) | # Creating a function from score to proba from Guillaume's values
p_cnv = util._build_dict_prob_cnv()
#print(p_cnv.keys())
#print(p_cnv.values())
#### Definition with a piecewise linear function
#score2prob = util.create_score2prob_lin_piecewise(p_cnv)
#scores = np.arange(15,50,1)
#probs = [score2prob(sc) for sc in sc... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Finally, putting things together | # re-loading
reload(util)
CWD = osp.join(osp.expanduser('~'), 'documents','grants_projects','roberto_projects', \
'guillaume_huguet_CNV','File_OK')
filename = 'Imagen_QC_CIA_MMAP_V2_Annotation.tsv'
fullfname = osp.join(CWD, filename)
# in numpy array
arr = np.loadtxt(fullfname, dtype='str', comments=No... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Create a dict of the cnv | from collections import OrderedDict
cnv = OrderedDict()
names_from = ["CHR de Merge_CIA_610_660_QC", 'START', 'STOP']
#, "5'gene", "3'gene", "5'dist(kb)", "3'dist(kb)"]
blank_dgr = 0
for line in arr[1:]:
lline = line.split('\t')
dgr = lline[i_DANGER]
scr = lline[i_SCORE]
cnv_... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Create a dictionary of the subjects - | cnv = OrderedDict({})
#names_from = ['START', 'STOP', "5'gene", "3'gene", "5'dist(kb)", "3'dist(kb)"]
names_from = ['IID_projet']
for line in arr[1:]:
lline = line.split('\t')
dgr = lline[i_DANGER]
scr = lline[i_SCORE]
sub_iid = util.make_uiid(line, names_from, arr[0])
if dgr != '':
a... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Histogram of the number of cnv used to compute dangerosity | print(len(cnv))
nbcnv = [len(cnv[sb]) for sb in cnv]
hist = plt.hist(nbcnv, bins=50)
print(np.max(np.asarray(nbcnv)))
# definition of dangerosity from a list of cnv
def dangerosity(listofcnvs):
"""
inputs: list tuples (danger_score, proba_cnv)
returns: a dangerosity score
"""
last = -1 #slicing th... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Testing dangerosity | for k in range(1,30, 30):
print(cnv[cnv.keys()[k]], ' yields ', dangerosity(cnv[cnv.keys()[k]]))
test_dangerosity_input = [[(1., .5), (1., .5), (1., .5), (1., .5)],
[(2., 1.)],
[(10000., 0.)]]
test_dangerosity_output = [2., 2., 0]
#print( [dangerosity(icnv) ... | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Printing out results | dtime = datetime.now().strftime("%y-%m-%d_h%H-%M")
outfile = dtime+'dangerosity_cnv.txt'
fulloutfile = osp.join(CWD, outfile)
with open(fulloutfile, 'w') as outf:
for sub in cnv:
outf.write("\t".join([sub, str(dangerosity(cnv[sub]))]) + "\n") | CNV_dangerosite.ipynb | jbpoline/cnv_analysis | artistic-2.0 |
Testing of playing pyguessgame.
Generates random numbers and plays a game.
Create two random lists of numbers 0/9,10/19,20/29 etc to 100.
Compare the two lists. If win mark, if lose mark.
Debian | #for ronum in ranumlis:
# print ronum
randict = dict()
othgues = []
othlow = 0
othhigh = 9
for ranez in range(10):
randxz = random.randint(othlow, othhigh)
othgues.append(randxz)
othlow = (othlow + 10)
othhigh = (othhigh + 10)
#print othgues
tenlis = ['zero', 'ten', 'twenty', 'thirty',... | pggNumAdd.ipynb | wcmckee/signinlca | mit |
Makes dict with keys pointing to the 10s numbers.
The value needs the list of random numbers updated.
Currently it just adds the number one.
How to add the random number list? | for ronum in ranumlis:
#print ronum
if ronum in othgues:
print (str(ronum) + ' You Win!')
else:
print (str(ronum) + ' You Lose!')
#dieci = dict()
#for ranz in range(10):
#print str(ranz) + str(1)#
# dieci.update({str(ranz) + str(1): str(ranz)})
# for numz in range(10):
... | pggNumAdd.ipynb | wcmckee/signinlca | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Creating a Pandas DataFrame from a CSV file<br></p> | data = pd.read_csv('./weather/daily_weather.csv') | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold">Daily Weather Data Description</p>
<br>
The file daily_weather.csv is a comma-separated file that contains weather data. This data comes from a weather station located in San Diego, California. The weather station is equipped with sensors t... | data.columns | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<br>Each row in daily_weather.csv captures weather data for a separate day. <br><br>
Sensor measurements from the weather station were captured at one-minute intervals. These measurements were then processed to generate values to describe daily weather. Since this dataset was created to classify low-humidity days vs.... | data
data[data.isnull().any(axis=1)] | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Data Cleaning Steps<br><br></p>
We will not need to number for each row so we can clean it. | del data['number'] | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
Now let's drop null values using the pandas dropna function. | before_rows = data.shape[0]
print(before_rows)
data = data.dropna()
after_rows = data.shape[0]
print(after_rows) | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
How many rows dropped due to cleaning?<br><br></p> | before_rows - after_rows | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold">
Convert to a Classification Task <br><br></p>
Binarize the relative_humidity_3pm to 0 or 1.<br> | clean_data = data.copy()
clean_data['high_humidity_label'] = (clean_data['relative_humidity_3pm'] > 24.99)*1
print(clean_data['high_humidity_label']) | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Target is stored in 'y'.
<br><br></p> | y=clean_data[['high_humidity_label']].copy()
#y
clean_data['relative_humidity_3pm'].head()
y.head() | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Use 9am Sensor Signals as Features to Predict Humidity at 3pm
<br><br></p> | morning_features = ['air_pressure_9am','air_temp_9am','avg_wind_direction_9am','avg_wind_speed_9am',
'max_wind_direction_9am','max_wind_speed_9am','rain_accumulation_9am',
'rain_duration_9am']
X = clean_data[morning_features].copy()
X.columns
y.columns | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Perform Test and Train split
<br><br></p>
REMINDER: Training Phase
In the training phase, the learning algorithm uses the training data to adjust the model’s parameters to minimize errors. At the end of the training phase, you get th... | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=324)
#type(X_train)
#type(X_test)
#type(y_train)
#type(y_test)
#X_train.head()
#y_train.describe() | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Fit on Train Set
<br><br></p> | humidity_classifier = DecisionTreeClassifier(max_leaf_nodes=10, random_state=0)
humidity_classifier.fit(X_train, y_train)
type(humidity_classifier) | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Predict on Test Set
<br><br></p> | predictions = humidity_classifier.predict(X_test)
predictions[:10]
y_test['high_humidity_label'][:10] | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
<p style="font-family: Arial; font-size:1.75em;color:purple; font-style:bold"><br>
Measure Accuracy of the Classifier
<br><br></p> | accuracy_score(y_true = y_test, y_pred = predictions) | Week-7-MachineLearning/Weather Data Classification using Decision Trees.ipynb | harishkrao/DSE200x | mit |
2 使用类(class)装饰器 | from functools import wraps
def singleton(cls):
instances = {}
@wraps(cls)
def wrapper(*args, **kwargs):
if cls not in instances:
instances[cls] = cls(*args, **kwargs)
return instances[cls]
return wrapper
@singleton
class MyClass(object):
pass
myclass1 ... | python-statatics-tutorial/advance-theme/Singleton.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
3 使用GetInstance方法，非线程安全 | class MySingleton(object):
@classmethod
def getInstance(cls):
if not hasattr(cls, '_instance'):
cls._instance = cls()
return cls._instance
mysingleton1 = MySingleton.getInstance()
mysingleton2 = MySingleton.getInstance()
print id(mysingleton1) == id(mysingleton2) | python-statatics-tutorial/advance-theme/Singleton.ipynb | gaufung/Data_Analytics_Learning_Note | mit |
Indefinite integrals
Here is a table of definite integrals. Many of these integrals has a number of parameters $a$, $b$, etc.
Find five of these integrals and perform the following steps:
Typeset the integral using LateX in a Markdown cell.
Define an integrand function that computes the value of the integrand.
Define ... | def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, np.inf, args=(a,))
return I
def integral_exact(a):
return 0.5*np.pi/a
print("Numerical: ", integral_approx(1.0))
prin... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Integral 1
\begin{equation}
\int_{0}^{a}{\sqrt{a^2 - x^2}} dx=\frac{\pi a^2}{4}
\end{equation} | # YOUR CODE HERE
def integrand(x, a):
return (np.sqrt(a**2 - x**2))
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, a, args=(a,))
return I
def integral_exact(a):
return (0.25*np.pi*a**2)
print("Numerical: ", int... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Integral 2
\begin{equation}
\int_{0}^{\infty} e^{-ax^2} dx =\frac{1}{2}\sqrt{\frac{\pi}{a}}
\end{equation} | # YOUR CODE HERE
def integrand(x, a):
return np.exp(-a*x**2)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, np.inf, args=(a,))
return I
def integral_exact(a):
return 0.5*np.sqrt(np.pi/a)
print("Numerical: ", in... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Integral 3
\begin{equation}
\int_{0}^{\infty} \frac{x}{e^x-1} dx =\frac{\pi^2}{6}
\end{equation} | # YOUR CODE HERE
def integrand(x, a):
return x/(np.exp(x)-1)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, np.inf, args=(a,))
return I
def integral_exact(a):
return (1/6.0)*np.pi**2
print("Numerical: ", integr... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Integral 4
\begin{equation}
\int_{0}^{\infty} \frac{x}{e^x+1} dx =\frac{\pi^2}{12}
\end{equation} | # YOUR CODE HERE
def integrand(x, a):
return x/(np.exp(x)+1)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, np.inf, args=(a,))
return I
def integral_exact(a):
return (1/12.0)*np.pi**2
print("Numerical: ", integ... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Integral 5
\begin{equation}
\int_{0}^{1} \frac{ln x}{1-x} dx =-\frac{\pi^2}{6}
\end{equation} | # YOUR CODE HERE
def integrand(x, a):
return np.log(x)/(1-x)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integrate.quad(integrand, 0, 1, args=(a,))
return I
def integral_exact(a):
return (-1.0/6.0)*np.pi**2
print("Numerical: ", integral... | assignments/assignment09/IntegrationEx02.ipynb | JackDi/phys202-2015-work | mit |
Lets analyze this graph:
- the first ir basic block has the name set to main
- it is composed of 2 AssignBlocks
- the first AssignBlock contains only one assignment, EAX = EBX
- the second one is IRDst = loc_key_1
The IRDst is a special register which represent a kind of program counter in intermediate representation. ... | graph_ir_x86("""
main:
ADD EAX, 3
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
In this graph, we can note that each instruction side effect is represented.
Note that in the equation:
zf = FLAG_EQ_CMP(EAX, -0x3)
The detailed version of the expression:
ExprId('zf', 1) = ExprOp('FLAG_EQ_CMP', ExprId('EAX', 32), ExprInt(-0x3, 32))
The operator FLAG_EQ_CMP is a kind of high level representation. But y... | graph_ir_x86("""
main:
XCHG EAX, EBX
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
This one is interesting, as it demonstrate perfectly the parallel execution of multiple assignments. In you are puzzled by this notation, imagine this describes equations, which expresses destination variables of an output state depending on an input state. The equations can be rewritten:
EAX_out = EBX_in
EBX_out = EAX... | # Here is a push
graph_ir_x86("""
main:
PUSH EAX
""")
graph_ir_x86("""
main:
CMOVZ EAX, EBX
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
Here are some remarks we can do on this version:
- one x86 instruction has generated multiple IRBlocks
- the first IRBlock only reads the zf (we don't take the locations into account here)
- EAX is assigned only in the case of zf equals to 1
- EBX is read only in the case of zf equals to 1
We can dispute on the fact th... | graph_ir_x86("""
main:
JZ end
MOV EAX, EBX
end:
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
The conclusion is that in intermediate representation, the cmovz is exactly as difficult as analyzing the code using jz/mov
So an important point is that in intermediate representation, one instruction can generate multiple IRBlocks. Here are some interesting examples: | graph_ir_x86("""
main:
MOVSB
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
And now, the version using a repeat prefix: | graph_ir_x86("""
main:
REP MOVSB
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
In the very same way as cmovz, if the rep movsb instruction didn't exist, we would use a more complex code.
The translation of some instructions are tricky: | graph_ir_x86("""
main:
SHR EAX, 1
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
For the moment, nothing special. EAX is updated correctly, and the flags are updated according to the result (note those side effects are in parallel here). But look at the next one: | graph_ir_x86("""
main:
SHR EAX, CL
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
In this case, if CL is zero, the destination is shifted by a zero amount. The instruction behaves (in 32 bit mode) as a nop, and the flags are not assigned. We could have done the same trick as in the cmovz, but this representation matches more accurately the instruction semantic.
Here is another one: | graph_ir_x86("""
main:
DIV ECX
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
This instruction may generate an exception in case of the divisor is zero. The intermediate representation generates a test in which it evaluate the divisor value and assigns a special register exception_flags to a constant. This constant represents the division by zero.
Note this is arbitrary. We could have done the c... | graph_ir_x86("""
main:
INT 0x3
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
Memory accesses by default explicit segmentation: | graph_ir_x86("""
main:
MOV EAX, DWORD PTR FS:[EBX]
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
The pointer of the memory uses the special operator segm, which takes two arguments:
- the value of the segment used the memory access
- the base address
Note that if you work in a flat segmentation model, you can add a post translation pass which will simplify ExprOp("segm", A, B) into B. This will ease code analysis.... | asmcfg = gen_x86_asmcfg("""
main:
CALL 0x11223344
MOV EBX, EAX
""")
asmcfg.graphviz()
graph_ir_x86("""
main:
CALL 0x11223344
MOV EBX, EAX
""") | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
What did happened here ?
- the call instruction has 2 side effects: stacking the return address and jumping to the subfunction address
- here, the subfunction address is 0x1122334455, and the return address is located at offset 0x5, which is represented here by loc_5
The question is: why are there unlinked nodes in the... | graph_ir_x86("""
main:
MOV EBX, 0x1234
CALL 0x11223344
MOV ECX, EAX
RET
""", True) | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
What happened here?
The translation of the call is replaced by two side effects which occur in parallel:
- EAX is set to the result of the operator call_func_ret which has two arguments: loc_11223344 and ESP
- ESP is set to the result of the operator call_func_stack which has two arguments: loc_11223344 and ESP
The fir... | # Construct a custom lifter
class LifterFixCallStack(LifterModelCall_x86_32):
def call_effects(self, addr, instr):
if addr.is_loc():
if self.loc_db.get_location_offset(addr.loc_key) == 0x11223344:
# Suppose the function at 0x11223344 has 3 arguments
... | doc/ir/lift.ipynb | serpilliere/miasm | gpl-2.0 |
Read File Containing Zones
Using the read_zbarray utility, we can import zonebudget-style array files. | from flopy.utils import read_zbarray
zone_file = os.path.join(loadpth, 'zonef_mlt')
zon = read_zbarray(zone_file)
nlay, nrow, ncol = zon.shape
fig = plt.figure(figsize=(10, 4))
for lay in range(nlay):
ax = fig.add_subplot(1, nlay, lay+1)
im = ax.pcolormesh(zon[lay, :, :])
cbar = plt.colorbar(im)
plt.... | examples/Notebooks/flopy3_ZoneBudget_example.ipynb | bdestombe/flopy-1 | bsd-3-clause |
Extract Budget Information from ZoneBudget Object
At the core of the ZoneBudget object is a numpy structured array. The class provides some wrapper functions to help us interogate the array and save it to disk. | # Create a ZoneBudget object and get the budget record array
zb = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=(0, 1096))
zb.get_budget()
# Get a list of the unique budget record names
zb.get_record_names()
# Look at a subset of fluxes
names = ['RECHARGE_IN', 'ZONE_1_IN', 'ZONE_3_IN']
zb.get_budget(names=names)
# Loo... | examples/Notebooks/flopy3_ZoneBudget_example.ipynb | bdestombe/flopy-1 | bsd-3-clause |
Convert Units
The ZoneBudget class supports the use of mathematical operators and returns a new copy of the object. | cmd = flopy.utils.ZoneBudget(cbc_f, zon, kstpkper=(0, 0))
cfd = cmd / 35.3147
inyr = (cfd / (250 * 250)) * 365 * 12
cmdbud = cmd.get_budget()
cfdbud = cfd.get_budget()
inyrbud = inyr.get_budget()
names = ['RECHARGE_IN']
rowidx = np.in1d(cmdbud['name'], names)
colidx = 'ZONE_1'
print('{:,.1f} cubic meters/day'.format... | examples/Notebooks/flopy3_ZoneBudget_example.ipynb | bdestombe/flopy-1 | bsd-3-clause |
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