markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Embedded Python in ObjectScriptFrom ObjectScript, run some Python library methods. | set datetime = ##class(%SYS.Python).Import("datetime")
zw datetime
zw datetime.date.today().isoformat() | datetime=3@%SYS.Python ; <module 'datetime' from '/usr/lib/python3.8/datetime.py'> ; <OREF>
"2021-12-12"
| MIT | src/Notebooks/ObjectScript.ipynb | gjsjohnmurray/iris-python-template |
Examples of usage of Gate Angle PlaceholderThe word "Placeholder" is used in Qubiter (we are in good company, Tensorflow uses this word in the same way) to mean a variable for which we delay/postpone assigning a numerical value (evaluating it) until a later time. In the case of Qubiter, it is useful to define gates wi... | import os
import sys
print(os.getcwd())
os.chdir('../../')
print(os.getcwd())
sys.path.insert(0,os.getcwd()) | C:\Users\rrtuc\Desktop\backedup\python-projects\qubiter\qubiter\jupyter-notebooks
C:\Users\rrtuc\Desktop\backedup\python-projects\qubiter
| Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
We begin by writing a simple circuit with 4 qubits. As usual, the following code willwrite an English and a Picture file in the `io_folder` directory. Note that someangles have been entered into the write() Python functions as legalvariable names instead of floats. In the English file, you will see those legalnames whe... | from qubiter.SEO_writer import *
from qubiter.SEO_reader import *
from qubiter.EchoingSEO_reader import *
from qubiter.SEO_simulator import *
num_bits = 4
file_prefix = 'placeholder_test'
emb = CktEmbedder(num_bits, num_bits)
wr = SEO_writer(file_prefix, emb)
wr.write_Rx(2, rads=np.pi/7)
wr.write_Rx(1, rads='#2*.5')
wr... | _____no_output_____ | Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
The following 2 files were just written:1. ../io_folder/placeholder_test_4_eng.txt2. ../io_folder/placeholder_test_4_ZLpic.txt Simply by creating an object of the class SEO_reader with the flag `write_log` set equal to True, you can create a log file which contains * a list of distinct variable numbers * a list of dist... | rdr = SEO_reader(file_prefix, num_bits, write_log=True) | _____no_output_____ | Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
The following log file was just written: ../io_folder/placeholder_test_4_log.txt Next, let us create two functions that will be used for the functional placeholders | def my_fun1(x):
return x*.5
def my_fun2(x, y):
return x + y | _____no_output_____ | Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
**Partial Substitution**This creates new fileswith `1=30`, `2=60`, `'my_fun1'->my_fun1`,but `3` and `'my_fun2'` still undecided | vman = PlaceholderManager(eval_all_vars=False,
var_num_to_rads={1: np.pi/6, 2: np.pi/3},
fun_name_to_fun={'my_fun1': my_fun1})
wr = SEO_writer(file_prefix + '_eval01', emb)
EchoingSEO_reader(file_prefix, num_bits, wr,
vars_manager=vman) | _____no_output_____ | Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
The following 2 files were just written:1. ../io_folder/placeholder_test_eval01_4_eng.txt2. ../io_folder/placeholder_test_eval01_4_ZLpic.txt The following code runs the simulator after substituting`1=30`, `2=60`, `3=90`, `'my_fun1'->my_fun1`, `'my_fun2'->my_fun2` | vman = PlaceholderManager(
var_num_to_rads={1: np.pi/6, 2: np.pi/3, 3: np.pi/2},
fun_name_to_fun={'my_fun1': my_fun1, 'my_fun2': my_fun2}
)
sim = SEO_simulator(file_prefix, num_bits, verbose=False,
vars_manager=vman)
StateVec.describe_st_vec_dict(sim.cur_st_vec_dict) | *********branch= pure
total probability of state vector (=one if no measurements)= 1.0000000000000004
dictionary with key=qubit, value=(Prob(0), Prob(1))
{0: (1.0000000000000004, -4.440892098500626e-16),
1: (0.7500000000000002, 0.24999999999999978),
2: (0.811744900929367, 0.18825509907063298),
3: (0.6235127414399703... | Apache-2.0 | qubiter/jupyter_notebooks/examples_of_placeholder_usage.ipynb | yourball/qubiter |
The art of using pipelines Pipelines are a natural way to think about a machine learning system. Indeed with some practice a data scientist can visualise data "flowing" through a series of steps. The input is typically some raw data which has to be processed in some manner. The goal is to represent the data in such a ... | from pprint import pprint
from river import datasets
for x, y in datasets.Restaurants():
pprint(x)
pprint(y)
break | {'area_name': 'Tōkyō-to Nerima-ku Toyotamakita',
'date': datetime.datetime(2016, 1, 1, 0, 0),
'genre_name': 'Izakaya',
'is_holiday': True,
'latitude': 35.7356234,
'longitude': 139.6516577,
'store_id': 'air_04341b588bde96cd'}
10
| BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
We'll start by building and running a model using a procedural coding style. The performance of the model doesn't matter, we're simply interested in the design of the model. | from river import feature_extraction
from river import linear_model
from river import metrics
from river import preprocessing
from river import stats
means = (
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)),
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)),
feat... | MAE: 8.465114
| BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
We're not using many features. We can print the last `x` to get an idea of the features (don't forget they've been scaled!) | pprint(x) | {'is_holiday': -0.23103573677646685,
'is_weekend': 1.6249280076334165,
'weekday': 1.0292832579142892,
'y_rollingmean_14_by_store_id': -1.4125913815779154,
'y_rollingmean_21_by_store_id': -1.3980979075298519,
'y_rollingmean_7_by_store_id': -1.3502314499809096}
| BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
The above chunk of code is quite explicit but it's a bit verbose. The whole point of libraries such as `river` is to make life easier for users. Moreover there's too much space for users to mess up the order in which things are done, which increases the chance of there being target leakage. We'll now rewrite our model ... | from river import compose
def get_date_features(x):
weekday = x['date'].weekday()
return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}
model = compose.Pipeline(
('features', compose.TransformerUnion(
('date_features', compose.FuncTransformer(get_date_features)),
('last_7_mean', ... | MAE: 8.38533
| BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
We use a `Pipeline` to arrange each step in a sequential order. A `TransformerUnion` is used to merge multiple feature extractors into a single transformer. The `for` loop is now much shorter and is thus easier to grok: we get the out-of-fold prediction, we fit the model, and finally we update the metric. This way of e... | from river import evaluate
model = compose.Pipeline(
('features', compose.TransformerUnion(
('date_features', compose.FuncTransformer(get_date_features)),
('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7))),
('last_14_mean', feature_extraction.TargetAgg(by... | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Notice that you couldn't have used the `progressive_val_score` method if you wrote the model in a procedural manner.Our code is getting shorter, but it's still a bit difficult on the eyes. Indeed there is a lot of boilerplate code associated with pipelines that can get tedious to write. However `river` has some special... | model = compose.Pipeline(
compose.TransformerUnion(
compose.FuncTransformer(get_date_features),
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)),
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)),
feature_extraction.TargetAgg(by='store_id', h... | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Under the hood a `Pipeline` inherits from `collections.OrderedDict`. Indeed this makes sense because if you think about it a `Pipeline` is simply a sequence of steps where each step has a name. The reason we mention this is because it means you can manipulate a `Pipeline` the same way you would manipulate an ordinary `... | for name in model.steps:
print(name) | TransformerUnion
Discard
StandardScaler
LinearRegression
| BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
The first step is a `FeatureUnion` and it's string representation contains the string representation of each of it's elements. Not having to write names saves up some time and space and is certainly less tedious.The next trick is that we can use mathematical operators to compose our pipeline. For example we can use the... | model = compose.Pipeline(
compose.FuncTransformer(get_date_features) + \
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)) + \
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)) + \
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21)),
compo... | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Likewhise we can use the `|` operator to assemble steps into a `Pipeline`. | model = (
compose.FuncTransformer(get_date_features) +
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)) +
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)) +
feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21))
)
to_discard = ['store_id', 'dat... | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Hopefully you'll agree that this is a powerful way to express machine learning pipelines. For some people this should be quite remeniscent of the UNIX pipe operator. One final trick we want to mention is that functions are automatically wrapped with a `FuncTransformer`, which can be quite handy. | model = get_date_features
for n in [7, 14, 21]:
model += feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(n))
model |= compose.Discard(*to_discard)
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression()
evaluate.progressive_val_score(datasets.Restaurants(), model, metri... | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Naturally some may prefer the procedural style we first used because they find it easier to work with. It all depends on your style and you should use what you feel comfortable with. However we encourage you to use operators because we believe that this will increase the readability of your code, which is very importan... | model | _____no_output_____ | BSD-3-Clause | docs/examples/the-art-of-using-pipelines.ipynb | dataJSA/river |
Reflect Tables into SQLAlchemy ORM | # Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
engine = create_engine("sqlite:///Resources/hawaii.sqlite")
# reflect an existing database into a new model
Base = automap_ba... | _____no_output_____ | MIT | climate_starter.ipynb | ahchambers/sqlalchemy-challenge |
Exploratory Climate Analysis | # Design a query to retrieve the last 12 months of precipitation data and plot the results
# Calculate the date 1 year ago from the last data point in the database
last_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)
# Perform a query to retrieve the data and precipitation scores
results = session.query(measurem... | _____no_output_____ | MIT | climate_starter.ipynb | ahchambers/sqlalchemy-challenge |
Photometric PluginFor optical photometry, we provide the **PhotometryLike** plugin that handles forward folding of a spectral model through filter curves. Let's have a look at the avaiable procedures. | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from threeML import *
# we will need XPSEC models for extinction
from astromodels.xspec import *
# The filter library takes a while to load so you must import it explicitly..
from threeML.plugins.photometry.filter_library import threeML_filter_li... | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
SetupWe use [speclite](http://speclite.readthedocs.io/en/latest/ ) to handle optical filters.Therefore, you can easily build your own custom filters, use the built in speclite filters, or use the 3ML filter library that we have built thanks to [Spanish Virtual Observatory](http://svo.cab.inta-csic.es/main/index.php). ... | import speclite.filters as spec_filters
my_backyard_telescope_filter = spec_filters.load_filter('bessell-r')
# NOTE:
my_backyard_telescope_filter.name | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
NOTE: the filter name is 'bessell-R'. The plugin will look for the name *after* the **'-'** i.e 'R'Now let's build a 3ML plugin via **PhotometryLike**. Our data are entered as keywords with the name of the filter as the keyword and the data in an magnitude,error tuple, i.e. R=(mag,mag_err): | my_backyard_telescope = PhotometryLike('backyard_astronomy',
filters=my_backyard_telescope_filter, # the filter
R=(20,.1) ) # the magnitude and error
my_backyard_telescope.display_filters() | Using Gaussian statistic (equivalent to chi^2) with the provided errors.
| BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
3ML filter libraryExplore the filter library. If you cannot find what you need, it is simple to add your own | threeML_filter_library.SLOAN
spec_filters.plot_filters(threeML_filter_library.SLOAN.SDSS)
spec_filters.plot_filters(threeML_filter_library.Herschel.SPIRE)
spec_filters.plot_filters(threeML_filter_library.Keck.NIRC2) | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
Build your own filtersFollowing the example from speclite, we can build our own filters and add them: | fangs_g = spec_filters.FilterResponse(
wavelength = [3800, 4500, 5200] * u.Angstrom,
response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name='g'))
fangs_r = spec_filters.FilterResponse(
wavelength = [4800, 5500, 6200] * u.Angstrom,
response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name=... | Using Gaussian statistic (equivalent to chi^2) with the provided errors.
| BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
GROND ExampleNow we will look at GROND. We get the filter from the 3ML filter library.(Just play with tab completion to see what is available!) | grond = PhotometryLike('GROND',
filters=threeML_filter_library.ESO.GROND,
#g=(21.5.93,.23), # we exclude these filters
#r=(22.,0.12),
i=(21.8,.01),
z=(21.2,.01),
J=(19.6,.01),
... | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
Model specificationHere we use XSPEC's dust extinction models for the milky way and the host | spec = Powerlaw() * XS_zdust() * XS_zdust()
data_list = DataList(grond)
model = Model(PointSource('grb',0,0,spectral_shape=spec))
spec.piv_1 = 1E-2
spec.index_1.fix=False
spec.redshift_2 = 0.347
spec.redshift_2.fix = True
spec.e_bmv_2 = 5./2.93
spec.e_bmv_2.fix = True
spec.rv_2 = 2.93
spec.rv_2.fix = True
spec.m... | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
We compute $m_{\rm AB}$ from astromodels photon fluxes. This is done by convolving the differential flux over the filter response:$ F[R,f_\lambda] \equiv \int_0^\infty \frac{dg}{d\lambda}(\lambda)R(\lambda) \omega(\lambda) d\lambda$where we have converted the astromodels functions to wavelength properly. | _ = jl.fit() | Best fit values:
| BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
We can now look at the fit in magnitude space or model space as with any plugin. | _=display_photometry_model_magnitudes(jl)
_ = plot_point_source_spectra(jl.results,flux_unit='erg/(cm2 s keV)',
xscale='linear',
energy_unit='nm',ene_min=1E3, ene_max=1E5, num_ene=200 ) | _____no_output_____ | BSD-3-Clause | examples/Photometry_demo.ipynb | ke-fang/3ML |
Copyright 2018 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
Tensorflow Lite Gesture Classification Example Conversion ScriptThis guide shows how you can go about converting the model trained with TensorFlowJS to TensorFlow Lite FlatBuffers.Run all steps in-order. At the end, `model.tflite` file will be downloaded. Run in Google Colab View source on... | !pip3 install tensorflow==1.14.0 keras==2.2.4 tensorflowjs==0.6.4 --force-reinstall
import traceback
import logging
import tensorflow.compat.v1 as tf
import keras.backend as K
import os
from google.colab import files
from keras import Model, Input
from keras.applications import MobileNet
from keras.engine.saving impo... | _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
***Cleanup any existing models if necessary*** | !rm -rf *.h5 *.tflite *.json *.bin | _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
**Upload your Tensorflow.js Artifacts Here**i.e., The weights manifest **model.json** and the binary weights file **model-weights.bin** | files.upload() | _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
**Export Configuration** | #@title Export Configuration
# TensorFlow.js arguments
config_json = "model.json" #@param {type:"string"}
weights_path_prefix = None #@param {type:"raw"}
model_tflite = "model.tflite" #@param {type:"string"}
| _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
**Model Converter**The following class converts a TensorFlow.js model to a TFLite FlatBuffer | class ModelConverter:
"""
Creates a ModelConverter class from a TensorFlow.js model file.
Args:
:param config_json_path: Full filepath of weights manifest file containing the model architecture.
:param weights_path_prefix: Full filepath to the directory in which the weights binaries exist.
... | _____no_output_____ | Apache-2.0 | lite/examples/gesture_classification/ml/tensorflowjs_to_tflite_colab_notebook.ipynb | hawk-praxs/examples |
Generate dataset | np.random.seed(12)
y = np.random.randint(0,10,5000)
idx= []
for i in range(10):
print(i,sum(y==i))
idx.append(y==i)
x = np.zeros((5000,2))
np.random.seed(12)
x[idx[0],:] = np.random.multivariate_normal(mean = [5,5],cov=[[0.1,0],[0,0.1]],size=sum(idx[0]))
x[idx[1],:] = np.random.multivariate_normal(mean = [-6,7]... | _____no_output_____ | MIT | AAAI/Learnability/CIN/Linear/ds2/size_100/synthetic_type2_Linear_m_50.ipynb | lnpandey/DL_explore_synth_data |
Introduction to Convolutional Neural Networks (CNNs) in PyTorch Representing images digitallyWhile convolutional neural networks (CNNs) see a wide variety of uses, they were originally designed for images, and CNNs are still most commonly used for vision-related tasks.For today, we'll primarily be focusing on CNNs fo... | %matplotlib inline
import imageio
import matplotlib.pyplot as plt
# Read the image "./Figures/chapel.jpg" from the disk.
# Hint: use `im = imageio.imread(<Path to the image>)`.
# Print the shape of the tensor
# Display the image | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
We can see that the image we loaded has height and width of $620 \times 1175$, with 3 channels corresponding to RGB.We can easily slice out and view individual color channels: | # Uncomment the following command to extract the red channel of the above image.
# im_red = im[:,:,0]
# Display the image
# Hint: To display the pixel values for a single channel, we can display the image using the gray-scale colormap
# Repeat the above for the blue channel to visualize features represented in the blu... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
While we have so far considered only 3 channel RGB images, there are many settings in which we may consider a different number of channels.For example, [hyperspectral imaging](https://en.wikipedia.org/wiki/Hyperspectral_imaging) uses a wide range of the electromagnetic spectrum to characterize a scene.Such modalities m... | import numpy as np
# PyTorch Imports
##################################################
# #
# ---- YOUR CODE HERE ---- #
# #
################################################## | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Review: Fully connected layerIn a fully connected layer, the input $x \in \mathbb R^{M \times C_{in}}$ is a vector (or, rather a batch of vectors), where $M$ is the minibatch size and $C_{in}$ is the dimensionality of the input. We first matrix multiply the input $x$ by a weight matrix $W$.This weight matrix has dimen... | # Create a random flat input vector
x_fc = torch.randn(100, 1024)
# Create weight matrix variable
W = torch.randn(1024, 10)/np.sqrt(1024)
# Create bias variable
b = torch.zeros(10, requires_grad=True)
# Use `W` and `b` to apply a fully connected layer.
# Store the output in variable `y`.
# Don't forget to apply the... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Convolutional layerIn a convolutional layer, we convolve the input $x$ with a convolutional kernel (aka filter), which we also call $W$, producing output $y$:\begin{align*}y = \text{ReLU}(W*x + b)\end{align*}In the context of CNNs, the output $y$ is often referred to as feature maps. As with a fully connected layer, t... | # Create a random 4D tensor. Use the NCHW format, where N = 100, C = 3, H = W =32
x_cnn =
# Create convolutional kernel variable (C_out, C_in, H_k, W_k)
W1 =
# Create a bias variable of size C_out
b1 =
# Apply the convolutional layer with relu activation
conv1 =
# Print input/output shape
print("Input shape: {}... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Just like in a MLP, we can stack multiple of these convolutional layers. In the *Representing Images Digitally* section, we briefly mentioned considering images with channels more than 3.Observe that the input to the second layer (i.e. the output of the first layer) can be viewed as an "image" with $C_{out}$ channels.I... | # Create the second convolutional layer by defining a random `W2` and `b2`
W2 =
b2 =
# Apply 2nd convolutional layer to the output of the first convolutional layer
conv2 =
# Print output shape
print("Second convolution output shape: {}".format(conv2.shape)) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
In fact, we typically perform these convolution operations many times. Popular CNN architectures for image analysis today can be 100+ layers. ReshapingYou'll commonly finding yourself needing to reshape tensors while building CNNs.The PyTorch function for doing so is `view()`. Anyone familiar with NumPy will find it v... | M = torch.zeros(4, 3)
M2 = M.view(1,1,12)
M3 = M.view(2,1,2,3)
M4 = M.view(-1,2,3)
M5 = M.view(-1) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
To get an idea of why reshaping is need in a CNN, let's look at a diagram of a simple CNN.First of all, the CNN expects a 4D input, with the dimensions corresponding to `[batch, channel, height, width]`.Your data may not come in this format, so you may have to reshape it yourself. | x_flat = torch.randn(100, 1024)
# Reshape flat input image into a 4D batched image input
# Hint: Use batch=100, height=width=32.
x_reshaped =
# Print input shape
print(x_reshaped.shape) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
CNN architectures also commonly contain fully connected layers or a softmax, as we're often interested in classification.Both of these expect 2D inputs with dimensions `[batch, dim]`, so you have to "flatten" a CNN's 4D output to 2D.For example, to flatten the convolutional feature maps we created earlier: | # Flatten convolutional feature maps into a vector
h_flat = conv2.view(-1, 32*32*32)
# Print output shape
print(h_flat.shape) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Pooling and stridingAlmost all CNN architectures incorporate either pooling or striding. This is done for a number of reasons, including:- Dimensionality reduction: pooling and striding operations reduces computational complexity by shrinking the number of values passed to the next layer.For example, a 2x2 maxpool red... | # Recreate the values in pooling figure with shape [4,4]
feature_map_fig =
# Convert 2D matrix to a 4D tensor of shape [1,1,4,4].
fmap_fig =
print("Feature map shape pre-pooling: {}".format(fmap_fig.shape))
# Apply max pool to fmap_fig
max_pool_fig =
print("\nMax pool")
print("Shape: {}".format(max_pool_fig.shap... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Now we will apply max pool and average pool to the output of the convolutional layer `conv2`. | # Taking the output we've been working with so far, first print its current size
print("Shape of conv2 feature maps before pooling: {0}".format(conv2.shape))
# Apply Max pool with size = 2 and then print new shape.
max_pool2 =
print("Shape of conv2 feature maps after max pooling: {0}".format(max_pool2.shape))
# Aver... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
StridingOne might expect that pixels in an image have high correlation with neighboring pixels, so we can save computation by skipping positions while sliding the convolutional kernel. By default, a CNN slides across the input one pixel at a time, which we call a stride of 1.By instead striding by 2, we skip calculati... | # Since striding is part of the convolution operation, we'll start with the feature maps before the 2nd convolution
print("Shape of conv1 feature maps: {0}".format(conv1.shape))
# Apply 2nd convolutional layer, with striding of 2
conv2_strided =
# Print output shape
print("Shape of conv2 feature maps with stride of ... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Building a custom CNN Let's revisit MNIST digit classification, but this time, we'll use the following CNN as our classifier: $5 \times 5$ convolution -> $2 \times 2$ max pool -> $5 \times 5$ convolution -> $2 \times 2$ max pool -> fully connected to $\mathbb R^{256}$ -> fully connected to $\mathbb R^{10}$ (prediction... | import torch.nn as nn
# Important: Inherit the `nn.Module` class to define a PyTorch model
class CIFAR_CNN():
def __init__(self):
super().__init__()
# Step 1: Define the first convoluation layer (C_in=3, C_out=32, H_k=W_k=5, padding = 2)
self.conv1 =
# Step 2: Def... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Notice how our `nn.Module` contains several operation chained together.The code for submodule initialization, which creates all the stateful parameters associated with each operation, is placed in the `__init__()` function, where it is run once during object instantiation.Meanwhile, the code describing the forward pass... | model = CIFAR_CNN()
print(model) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
We can drop this model into our logistic regression training code, with few modifications beyond changing the model itself.A few other changes:- CNNs expect a 4-D input, so we no longer have to reshape the images before feeding them to our neural network.- Since CNNs are a little more complex than models we've worked w... | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from tqdm.notebook import tqdm, trange
cifar_train = datasets.CIFAR10(root="./datasets/cifar-10/", train=True, transform=transforms.ToTensor(), download=True)
cifar_test = datasets.CIFAR10... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Let's plot the loss function | ##################################################
# #
# ---- YOUR CODE HERE ---- #
# #
################################################## | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Testing the trained model | ## Testing
correct = 0
total = len(cifar_test)
with torch.no_grad():
# Iterate through test set minibatchs
for images, labels in tqdm(test_loader):
# Step 1: Forward pass to get
y =
# Step 2: Compute the predicted labels from `y`.
predictions =
... | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
If you are running this notebook on CPU, training this CNN might take a while.On the other hand, if you use a GPU, this model should train in seconds.This is why we usually prefer to use GPUs when we have them. Torchvision Datasets and transformsAs any experienced ML practioner will say, data wrangling is often half ... | from torchvision import datasets
mnist_train = datasets.CIFAR10(root="./datasets", train=True, transform=transforms.ToTensor(), download=True) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
Of course, there's [many more](https://pytorch.org/vision/stable/datasets.html).Currently, datasets for image classification (e.g. MNIST, CIFAR, ImageNet), object detection (VOC, COCO, Cityscapes), and video action recognition (UCF101, Kinetics) are included.For formatting, pre-processing, and augmenting, [transforms](... | import torchvision.models as models
resnet18 = models.resnet18()
print(resnet18) | _____no_output_____ | MIT | day2_student_notebook.ipynb | dukeplusds/mlwscv2002 |
gpu info | gtx950 = DeviceInfo()
gtx950.sm_num = 6
gtx950.sharedmem_per_sm = 49152
gtx950.reg_per_sm = 65536
gtx950.maxthreads_per_sm = 2048 | _____no_output_____ | MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
single stream info | data_size = 23000
trace_file = './1cke/trace_' + str(data_size) + '.csv'
df_trace = trace2dataframe(trace_file) # read the trace to the dataframe
df_trace
df_single_stream = model_param_from_trace_v1(df_trace)
df_single_stream.head(20)
df_s1 = reset_starting(df_single_stream)
df_s1 | _____no_output_____ | MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
running 2cke case | stream_num = 2
df_cke_list = []
for x in range(stream_num):
df_cke_list.append(df_s1.copy(deep=True))
df_cke_list[0]
df_cke_list[1]
H2D_H2D_OVLP_TH = 3.158431
for i in range(1,stream_num):
# compute the time for the init data transfer
stream_startTime = find_whentostart_comingStream(df_cke_list[i-1], H2D_... | _____no_output_____ | MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
check whether there is h2d overlapping | prev_stm_h2ds_start, prev_stm_h2ds_end = find_h2ds_timing(df_cke_list[0])
print("prev stream h2ds : {} - {}".format(prev_stm_h2ds_start, prev_stm_h2ds_end))
curr_stm_h2ds_start, curr_stm_h2ds_end = find_h2ds_timing(df_cke_list[1])
print("curr stream h2ds : {} - {}".format(curr_stm_h2ds_start, curr_stm_h2ds_end))
if cu... | h2ds_ovlp_between_stream : False
| MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
check kernel overlapping | prev_stm_kern_start, prev_stm_kern_end = find_kern_timing(df_cke_list[0])
print("prev stream kern : {} - {}".format(prev_stm_kern_start, prev_stm_kern_end))
curr_stm_kern_start, curr_stm_kern_end = find_kern_timing(df_cke_list[1])
print("curr stream kern : {} - {}".format(curr_stm_kern_start, curr_stm_kern_end))
if ... | kern_ovlp_between_stream : True
| MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
use cke model if kern_ovlp_between_stream is true | # get the overlapping kernel info from both stream
kernel_ = model_cke_from_same_kernel(gtx950, df_trace, ) | _____no_output_____ | MIT | mem_mem/t2-cke.ipynb | 3upperm2n/trans_kernel_model |
Reflect Tables into SQLAlchemy ORM | # Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
engine = create_engine("sqlite:///Resources/hawaii.sqlite")
# reflect an existing database into a new model
Base = automap_bas... | _____no_output_____ | ADSL | sql_alchemy.ipynb | Yuva38/sqlalchemy-challenge |
Exploratory Climate Analysis using pandas | # Design a query to retrieve the last 12 months of precipitation data and plot the results
# Calculate the date 1 year ago from the last data point in the database
# Perform a query to retrieve the data and precipitation scores
# Save the query results as a Pandas DataFrame and set the index to the date column
# So... | _____no_output_____ | ADSL | sql_alchemy.ipynb | Yuva38/sqlalchemy-challenge |
Tutorial - Time Series Forecasting - Autoregression (AR)The goal is to forecast time series with the Autoregression (AR) Approach. 1) JetRail Commuter, 2) Air Passengers, 3) Function Autoregression with Air Passengers, and 5) Function Autoregression with Wine Sales.References Jason Brownlee - https://machinelearningma... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime
import warnings
warnings.filterwarnings("ignore")
# Load File
url = 'https://raw.githubusercontent.com/tristanga/Machine-Learning/master/Data/JetRail%20Avg%20Hourly%20Traffic%20Data%20-%202012-2013.csv'
df = pd.read_csv(url)
df.inf... | _____no_output_____ | MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
Autoregression (AR) Approach with JetRail The autoregression (AR) method models the next step in the sequence as a linear function of the observations at prior time steps.The notation for the model involves specifying the order of the model p as a parameter to the AR function, e.g. AR(p). For example, AR(1) is a first... | #Split Train Test
import math
total_size=len(df)
split = 10392 / 11856
train_size=math.floor(split*total_size)
train=df.head(train_size)
test=df.tail(len(df) -train_size)
from statsmodels.tsa.ar_model import AR
model = AR(train.Count)
fit1 = model.fit()
y_hat = test.copy()
y_hat['AR'] = fit1.predict(start=len(train), e... | _____no_output_____ | MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
RMSE Calculation | from sklearn.metrics import mean_squared_error
from math import sqrt
rms = sqrt(mean_squared_error(test.Count, y_hat.AR))
print('RMSE = '+str(rms)) | RMSE = 28.635096626807453
| MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
Autoregression (AR) Approach with Air Passagers | # Subsetting
url = 'https://raw.githubusercontent.com/tristanga/Machine-Learning/master/Data/International%20Airline%20Passengers.csv'
df = pd.read_csv(url, sep =";")
df.info()
df.Month = pd.to_datetime(df.Month,format='%Y-%m')
df.index = df.Month
#df.head()
#Creating train and test set
import math
total_size=len(df)
... | RMSE = 60.13838110500644
| MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
Function Autoregression (AR) Approach with variables | def AR_forecasting(mydf,colval,split):
#print(split)
import math
from statsmodels.tsa.api import Holt
from sklearn.metrics import mean_squared_error
from math import sqrt
global y_hat, train, test
total_size=len(mydf)
train_size=math.floor(split*total_size) #(70% Dataset)
train=mydf.... | _____no_output_____ | MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
Testing Function Autoregression (AR) Approach with Wine Dataset | url = 'https://raw.githubusercontent.com/tristanga/Data-Cleaning/master/Converting%20Time%20Series/Wine_Sales_R_Dataset.csv'
df = pd.read_csv(url)
df.info()
df.Date = pd.to_datetime(df.Date,format='%Y-%m-%d')
df.index = df.Date
AR_forecasting(df,'Sales',0.7) | _____no_output_____ | MIT | Time Series Analysis/Time Series Forecasting - Autoregression (AR)/Autoregression (AR).ipynb | shreejitverma/Data-Scientist |
import numpy as np
# Vector 1-D array
a = [1,2,3]
a = a + [1]
print(a)
# Numpy array 1-D
b = np.array([4,5,6])
b = np.append(b,[7])
A = np.array([[1,22,3],[4,5,6],[111,-11,33]])
B = np.array([[10,11,12],[13,14,15],[14,7,2.5]])
A.shape
sum = np.sum(np.dot(A,B))
print(sum)
sum.dtype
C = np.arr... | [[[0 0 0]
[0 0 0]
[0 0 0]]
[[0 0 0]
[0 0 0]
[0 0 0]]
[[0 0 0]
[0 0 0]
[0 0 0]]
[[0 0 0]
[0 0 0]
[0 0 0]]]
Number of Dimensions 3
Size of Array 36
| MIT | numpy.ipynb | OmidMustafa/XOR_python | |
Copyright 2019 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Introduzione a TensorFlow 2 per esperti Visualizza su TensorFlow.org Esegui in Google Colab Visualizza il sorgente su GitHub Scarica il notebook Note: La nostra comunità di Tensorflow ha tradotto questi documenti. Poichè queste traduzioni sono *best-effort*, non è garantito che rispecchino... | import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Carica e prepara il [dataset MNIST](http://yann.lecun.com/exdb/mnist/). | mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis] | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Usa `tf.data` per raggruppare e mischiare il dataset: | train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32) | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Costrusci il modello `tf.keras` usando l'[API Keras per creare sottoclassi di modelli](https://www.tensorflow.org/guide/kerasmodel_subclassing): | class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
... | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Scegli un metodo di ottimizzazione e una funzione obiettivo per l'addestramento: | loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam() | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Seleziona delle metriche per misurare la pertita e l'accuratezza del modello. Queste metriche accumulano i valori alle varie epoche e alla fine stampano il risultato globale. | train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy') | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Usa `tf.GradientTape` per addestrare il modello: | @tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
predictions = model(images)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accur... | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
Testa il modello: | @tf.function
def test_step(images, labels):
predictions = model(images)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
EPOCHS = 5
for epoch in range(EPOCHS):
for images, labels in train_ds:
train_step(images, labels)
for test_images, test_labels in test... | _____no_output_____ | Apache-2.0 | site/it/tutorials/quickstart/advanced.ipynb | justaverygoodboy/docs-l10n |
用带有三种类型噪声(度，边权重，点权重)的传销模型网络测试RoleMagnet的抗噪性 | import numpy as np
import networkx as nx
import matplotlib.pyplot as plt | _____no_output_____ | MIT | experiment_3.ipynb | Tirami-su/rolemagnet |
Creating a graph模拟23人的小型传销组织，带少量噪声 | %matplotlib inline
plt.rcParams['figure.dpi'] = 150
plt.rcParams['figure.figsize'] = (4, 3)
G = nx.DiGraph()
G.add_weighted_edges_from([('11','s1',0.07),('12','s1',0.1),('13','s1',0.06),('14','s1',0.09),('15','s1',0.08),
('21','s2',0.07),('22','s2',0.1),('23','s2',0.06),('24','s2',0.09),('25... | _____no_output_____ | MIT | experiment_3.ipynb | Tirami-su/rolemagnet |
RoleMagnet | import rolemagnet as rm
vec,role,label=rm.role_magnet(G, balance=balance) | Embedding: 100.00% -
SOM shape: [11, 7]
Training SOM: 145
| MIT | experiment_3.ipynb | Tirami-su/rolemagnet |
Visualization可视化节点的向量表示，用PCA降到二维后再次可视化 | print ('三维嵌入结果')
for i in range(len(G.nodes)):
print (list(G.nodes)[i],'\t',vec[i])
from mpl_toolkits.mplot3d import Axes3D
coord = np.transpose(vec)
fig = plt.figure(figsize=(4,3))
ax = Axes3D(fig)
ax.scatter(coord[0], coord[1], coord[2], c=color, s=150)
plt.show()
# 再次降到二维
from sklearn.decomposition import ... | 三维嵌入结果
11 [-4.656918 2.50780243 -2.60384377]
s1 [13.6955635 -7.36617524 0. ]
12 [-3.4945784 1.04689359 -3.71977681]
13 [-4.72707781 3.05246777 -2.23186608]
14 [-3.95635282 1.50288243 -3.34779913]
15 [-4.37087035 1.9886804 -2.97582145]
21 [-4.5643264 3.33294681 -2.60384377]
s2 [ 17.36... | MIT | experiment_3.ipynb | Tirami-su/rolemagnet |
Evaluation用 Adjusted Rand Index 和 V-Measure 两种指标评价聚类结果 | from sklearn.metrics.cluster import adjusted_rand_score, homogeneity_completeness_v_measure
true_label=[1,2,1,1,1,1,
1,2,1,1,1,1,1,
1,2,1,1,1,1,1,
3,4,5,6,6,6,6,7,7]
print('Adjusted Rand Index:',adjusted_rand_score(true_label,label))
print('V-Measure:',homogeneity_completeness_v_me... | Adjusted Rand Index: 0.9892723141150981
V-Measure: (1.0, 0.9536171907216509, 0.9762579846765088)
聚类结果
21 [-0.6 -0.4]
11
12
13
14
15
21
22
24
23
25
26
31
32
33
34
35
36
45 [1.2 1.2]
s1
59 [1.6 1.2]
s2
s3
41 [0.6 3.2]
mid
71 [ 3.6 -0.2]
... | MIT | experiment_3.ipynb | Tirami-su/rolemagnet |
Euler Problem 14================The following iterative sequence is defined for the set of positive integers: n → n/2 (n is even) n → 3n + 1 (n is odd)Using the rule above and starting with 13, we generate the following sequence: 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1It can be seen that this sequence (star... | D = {1:0}
maxlen = 0
start = 1
def collatz(n):
if n in D:
return D[n]
elif (n % 2):
c = 1 + collatz(3*n+1)
else:
c = 1 + collatz(n/2)
D[n] = c
return c
for n in range(1,1000000):
c = collatz(n)
if c > maxlen:
maxlen = c
start = n
print(start) | 837799
| MIT | Euler 014 - Longest Collatz Sequence.ipynb | Radcliffe/project-euler |
Relatório de Análise IV Seleções e Frequências | import pandas as pd
dados = pd.read_csv('dados/aluguel_residencial.csv', sep = ';')
dados.head(10)
# Selecione somente os imóveis classificados com tipo 'Apartamento'
selecao = dados['Tipo'] == 'Apartamento'
n1 = dados[selecao].shape[0]
n1
# Selecione os imóveis classificados com tipos 'Casa', 'Casa de Condomínio' e 'C... | Nº de imóveis classificados com tipo 'Apartamento' -> 19532
Nº de imóveis classificados com tipos 'Casa', 'Casa de Condomínio' e 'Casa de Vila' -> 2212
Nº de imóveis com área entre 60 e 100 metros quadrados, incluindo os limites -> 8719
Nº de imóveis que tenham pelo menos 4 quartos e aluguel menor que R$ 2.000,00 -> 41... | MIT | FormacaoPythonParaDataScience/PythonPandas-TratandoAnalisandoDados/CursoPandas/SelecoesFrequencias.ipynb | anablima/TreinamentosAlura |
Scrumblet(Courtesy of K Polansky)Two-step doublet score processing, mirroring the approach from Popescu et al. https://www.nature.com/articles/s41586-019-1652-y which was closely based on Pijuan-Sala et al. https://www.nature.com/articles/s41586-019-0933-9The first step starts with some sort of doublet score, e.g. Scru... | path_to_data = '/nfs/users/nfs_l/lg18/team292/lg18/gonads/data/scRNAseq/FCA/rawdata/'
metadata = pd.read_csv(path_to_data + 'immune_meta.csv', index_col=0)
metadata['process'].value_counts()
# Select process = CD45+
metadata_enriched = metadata[metadata['process'] == 'CD45+']
metadata_enriched
metadata_enriched['stage... | FCA_GND8784459
| MIT | immune_CD45enriched_load_detect_doublets.ipynb | ventolab/HGDA |
The BasicsAt the core of Python (and any programming language) there are some key characteristics of how a program is structured that enable the proper execution of that program. These characteristics include the structure of the code itself, the core data types from which others are built, and core operators that mod... | # The interpreter can be used as a calculator, and can also echo or concatenate strings.
3 + 3
3 * 3
3 ** 3
3 / 2 # classic division - output is a floating point number
# Use quotes around strings, single or double, but be consistent to the extent possible
'dogs'
"dogs"
"They're going to the beach"
'He said "I like m... | Hello World!
| Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
Try It YourselfGo to the section _4.4. Numeric Types_ in the Python 3 documentation at . The table in that section describes different operators - try some!What is the difference between the different division operators (`/`, `//`, and `%`)? VariablesVariables allow us to store values for later use. | a = 5
b = 10
a + b | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
Variables can be reassigned: | b = 38764289.1097
a + b | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
The ability to reassign variable values becomes important when iterating through groups of objects for batch processing or other purposes. In the example below, the value of `b` is dynamically updated every time the `while` loop is executed: | a = 5
b = 10
while b > a:
print("b="+str(b))
b = b-1 | b=10
b=9
b=8
b=7
b=6
| Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
Variable data types can be inferred, so Python does not require us to declare the data type of a variable on assignment. | a = 5
type(a) | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
is equivalent to | a = int(5)
type(a)
c = 'dogs'
print(type(c))
c = str('dogs')
print(type(c)) | <class 'str'>
<class 'str'>
| Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
There are cases when we may want to declare the data type, for example to assign a different data type from the default that will be inferred. Concatenating strings provides a good example. | customer = 'Carol'
pizzas = 2
print(customer + ' ordered ' + pizzas + ' pizzas.') | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
Above, Python has inferred the type of the variable `pizza` to be an integer. Since strings can only be concatenated with other strings, our print statement generates an error. There are two ways we can resolve the error:1. Declare the `pizzas` variable as type string (`str`) on assignment or2. Re-cast the `pizzas` var... | customer = 'Carol'
pizzas = str(2)
print(customer + ' ordered ' + pizzas + ' pizzas.')
customer = 'Carol'
pizzas = 2
print(customer + ' ordered ' + str(pizzas) + ' pizzas.') | Carol ordered 2 pizzas.
| Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
Given the following variable assignments:```x = 12y = str(14)z = donuts```Predict the output of the following:1. `y + z`2. `x + y`3. `x + int(y)`4. `str(x) + y`Check your answers in the interpreter. Variable Naming RulesVariable names are case senstive and:1. Can only consist of one "word" (no spaces).2. Must begin wit... | # Read unstructured text
# One way is to open the whole file as a block
file_path = "./beowulf" # We can save the path to the file as a variable
file_in = open(file_path, "r") # Options are 'r', 'w', and 'a' (read, write, append)
beowulf_a = file_in.read()
file_in.close()
print(beowulf_a)
# Another way is to read the ... | Asymmetry Ellipticity AvgLength (cm) Number of images \
count 1400.000000 1400.000000 1400.000000 1400.000000
mean 0.148230 0.384384 3.426853 9.320714
std 0.071228 0.089594 2.161549 20.747693
min 0.001400 0.096700 1.196... | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
StructureNow that we have practiced assigning variables and reading information from files, we will have a look at concepts that are key to developing processes to use and analyze this information. BlocksThe structure of a Python program is pretty simple:Blocks of code are defined using indentation. Code that is at a ... | # Fun with types
this = 12
that = 15
the_other = "27"
my_stuff = [this,that,the_other,["a","b","c",4]]
more_stuff = {
"item1": this,
"item2": that,
"item3": the_other,
"item4": my_stuff
}
this + that
# this won't work ...
# this + that + the_other
# ... but this will ...
this + that + int(the_othe... | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
ListsLists are a type of collection in Python. Lists allow us to store sequences of items that are typically but not always similar. All of the following lists are legal in Python: | # Separate list items with commas!
number_list = [1, 2, 3, 4, 5]
string_list = ['apples', 'oranges', 'pears', 'grapes', 'pineapples']
combined_list = [1, 2, 'oranges', 3.14, 'peaches', 'grapes', 99.19876]
# Nested lists - lists of lists - are allowed.
list_of_lists = [[1, 2, 3],
['oranges', 'grapes... | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
There are multiple ways to create a list: | # Create an empty list
empty_list = []
# As we did above, by using square brackets around a comma-separated sequence of items
new_list = [1, 2, 3]
# Using the type constructor
constructed_list = list('purple')
# Using a list comprehension
result_list = [i for i in range(1, 20)] | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
We can inspect our lists: | empty_list
new_list
result_list
constructed_list | _____no_output_____ | Apache-2.0 | 1.2-The Basics.ipynb | unmrds/cc-python |
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