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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From the above graph we can interpret that majority of the people are High School passouts and this is true for both Males and Females Bivariate Analysis | #Pairplot of all variables | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**In the above plot scatter diagrams are plotted for all the numerical columns in the dataset. A scatter plot is a visual representation of the degree of correlation between any two columns. The pair plot function in seaborn makes it very easy to generate joint scatter plots for all the columns in the data.** | df.corr() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Correlation Heatmap Normalizing and Scaling **Often the variables of the data set are of different scales i.e. one variable is in millions and other in only 100. For e.g. in our data set Income is having values in thousands and age in just two digits. Since the data in these variables are of different scales, it is t... | #Scales the data. Essentially returns the z-scores of every attribute
df.head() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**If you look at the variables INCOME, TRAVEL TIME and CAR AGE, all has been normalized and scaled in one scale now.** ENCODING**One-Hot-Encoding is used to create dummy variables to replace the categories in a categorical variable into features of each category and represent it using 1 or 0 based on the presence or a... | columns=["MARITAL STATUS", "SEX","EDUCATION","JOB","USE","CAR TYPE","CITY"]
df = pd.concat([df, dummies], axis=1)
# drop original column "fuel-type" from "df"
df.head() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Analysing tabular data We are going to use a LIBRARY called numpy We are going to use a LIBRARY called numpy | import numpy
numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',') | _____no_output_____ | MIT | 01-analysing-data.ipynb | onatemarta/thursday |
Variables | weight_kg = 55
print (weight_kg)
print ('Weight in pounds: ', weight_kg * 2.2)
weight_kg = 57.5
print ('New weight: ', weight_kg * 2.2)
%whos
data = numpy.loadtxt(fname='data/weather-01.csv', delimiter = ',')
print (data)
print (type(data))
%whos
# Finding out the data type
print(data.dtype)
# Find out the shape
print ... | _____no_output_____ | MIT | 01-analysing-data.ipynb | onatemarta/thursday |
print (triplesmallchunk) | print (triplesmallchunk)
print (numpy.mean(data))
print (numpy.max(data))
print (numpy.min(data))
# Get a set of data for the first station
station_0 = data [0, :]
print (numpy.max(station_0))
# We don't need to create 'temporaty' array slices
# We can refer to what we call array axes
# axis = 0 gets the mean DOWN each... | _____no_output_____ | MIT | 01-analysing-data.ipynb | onatemarta/thursday |
Task:* Produce maximum and minimum plots of this data* What do you think? | max_temperature = numpy.max (data, axis = 0)
min_temperature = numpy.min (data, axis = 0)
max_plot = matplotlib.pyplot.plot(max_temperature)
min_plot = matplotlib.pyplot.plot(min_temperature) | _____no_output_____ | MIT | 01-analysing-data.ipynb | onatemarta/thursday |
import sys
IN_COLAB = 'google.colab' in sys.modules
print('Google Colab? ' + str(IN_COLAB))
if not IN_COLAB:
#!python -m pip show tensorflow
!which python
!python -m pip show tensorflow
!pwd
from google.colab import drive
drive.mount("/content/gdrive")
!ls "/content/gdrive/My Drive/cancer_detection/metastatic_c... | _____no_output_____ | MIT | Chapter 04/vgg19_all_images_25_epochs_colab_modelfit.ipynb | bpbpublications/Mastering-TensorFlow-2.x | |
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/applications/vgg19 | # Imports
import numpy as np
import pandas as pd
from glob import glob
from skimage.io import imread
import os
import shutil
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, roc_auc_score
from sklearn.model_selection import train_test_split
import tensorflow
from tensorflow.keras.preproce... | /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2035: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.
warnings.warn('`Model.predict_generator` is deprecated and '
| MIT | Chapter 04/vgg19_all_images_25_epochs_colab_modelfit.ipynb | bpbpublications/Mastering-TensorFlow-2.x |
Convolutional Neural Networks: Step by StepWelcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. **Notation**:- Superscript $[l]$ denotes an object of the $l^{th}$... | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1) | _____no_output_____ | MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
2 - Outline of the AssignmentYou will be implementing the building blocks of a convolutional neural network! Each function you will implement will have detailed instructions that will walk you through the steps needed:- Convolution functions, including: - Zero Padding - Convolve window - Convolution forward ... | # GRADED FUNCTION: zero_pad
def zero_pad(X, pad):
"""
Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image,
as illustrated in Figure 1.
Argument:
X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m images
pad -- i... | x.shape = (4, 3, 3, 2)
x_pad.shape = (4, 7, 7, 2)
x[1,1] = [[ 0.90085595 -0.68372786]
[-0.12289023 -0.93576943]
[-0.26788808 0.53035547]]
x_pad[1,1] = [[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]
[ 0. 0.]]
| MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output**: **x.shape**: (4, 3, 3, 2) **x_pad.shape**: (4, 7, 7, 2) **x[1,1]**: [[ 0.90085595 -0.68372786] [-0.12289023 -0.93576943]... | # GRADED FUNCTION: conv_single_step
def conv_single_step(a_slice_prev, W, b):
"""
Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation
of the previous layer.
Arguments:
a_slice_prev -- slice of input data of shape (f, f, n_C_prev)
W -- Weight ... | Z = -6.99908945068
| MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output**: **Z** -6.99908945068 3.3 - Convolutional Neural Networks - Forward passIn the forward pass, you will take many filters and convolve them on the input. Each 'convolution' gives you a 2D matrix output. You will then stack these outputs to... | # GRADED FUNCTION: conv_forward
def conv_forward(A_prev, W, b, hparameters):
"""
Implements the forward propagation for a convolution function
Arguments:
A_prev -- output activations of the previous layer, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
W -- Weights, numpy array of shap... | Z's mean = 0.0489952035289
Z[3,2,1] = [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437
5.18531798 8.75898442]
cache_conv[0][1][2][3] = [-0.20075807 0.18656139 0.41005165]
| MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output**: **Z's mean** 0.0489952035289 **Z[3,2,1]** [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437 5.18531798 8.75898442] **cache_conv... | # GRADED FUNCTION: pool_forward
def pool_forward(A_prev, hparameters, mode = "max"):
"""
Implements the forward pass of the pooling layer
Arguments:
A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)
hparameters -- python dictionary containing "f" and "stride"
mod... | mode = max
A = [[[[ 1.74481176 0.86540763 1.13376944]]]
[[[ 1.13162939 1.51981682 2.18557541]]]]
mode = average
A = [[[[ 0.02105773 -0.20328806 -0.40389855]]]
[[[-0.22154621 0.51716526 0.48155844]]]]
| MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output:** A = [[[[ 1.74481176 0.86540763 1.13376944]]] [[[ 1.13162939 1.51981682 2.18557541]]]] A = [[[[ 0.02105773 -0.20328806 -0.40389855]]] [[[-0.22154621 0.51716526 0.48155844]]]] Congratulations! You have now i... | def conv_backward(dZ, cache):
"""
Implement the backward propagation for a convolution function
Arguments:
dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)
cache -- cache of values needed for the conv_backward(), output of conv... | _____no_output_____ | MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
** Expected Output: ** **dA_mean** 1.45243777754 **dW_mean** 1.72699145831 **db_mean** 7.83923256462 5.2 Pooling layer - backward pas... | def create_mask_from_window(x):
"""
Creates a mask from an input matrix x, to identify the max entry of x.
Arguments:
x -- Array of shape (f, f)
Returns:
mask -- Array of the same shape as window, contains a True at the position corresponding to the max entry of x.
"""
###... | _____no_output_____ | MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output:** **x =**[[ 1.62434536 -0.61175641 -0.52817175] [-1.07296862 0.86540763 -2.3015387 ]] **mask =**[[ True False False] [False False False]] Why do we keep track of the position of the max? It's because this is the input value that ultimately influenced the output, and therefore the cost. Backpro... | def distribute_value(dz, shape):
"""
Distributes the input value in the matrix of dimension shape
Arguments:
dz -- input scalar
shape -- the shape (n_H, n_W) of the output matrix for which we want to distribute the value of dz
Returns:
a -- Array of size (n_H, n_W) for which we dis... | _____no_output_____ | MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
**Expected Output**: distributed_value =[[ 0.5 0.5] [ 0.5 0.5]] 5.2.3 Putting it together: Pooling backward You now have everything you need to compute backward propagation on a pooling layer.**Exercise**: Implement the `pool_backward` function in both modes (`"max"` and `"average"`). You will once again use 4 for... | def pool_backward(dA, cache, mode = "max"):
"""
Implements the backward pass of the pooling layer
Arguments:
dA -- gradient of cost with respect to the output of the pooling layer, same shape as A
cache -- cache output from the forward pass of the pooling layer, contains the layer's input and h... | _____no_output_____ | MIT | doc/courses/coursera/deep learning specialization/Convolutional Neural Networks/Convolution+model+-+Step+by+Step+-+v2.ipynb | junhan/learnmachinelearning |
Imports | # Pandas, Numpy and Matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import All nltk
import nltk
#nltk.download_shell() | _____no_output_____ | Unlicense | notebooks/22_0_L_ExploratoryDataAnalysis.ipynb | luiservela/AstraZeneca |
Get tagged words | # Set name of file
filename = '../data/interim/disease_tags.pkl'
# Read to DataFrame
df = pd.read_pickle(filename)
# Echo
df.head()
# Drop nulls, exclude start/end/disease_tag columns
tags = df['Id ont unique_id'.split()].dropna(axis=0)
# Rename fields, create combined field ont:unique_id
tags['summary_id'] = tags['... | _____no_output_____ | Unlicense | notebooks/22_0_L_ExploratoryDataAnalysis.ipynb | luiservela/AstraZeneca |
Create links between tags in same summary | links = set()
for index, record in df.iterrows():
for tag1 in record['Tags']:
for tag2 in record['Tags']:
links.add((tag1, tag2))
len(links)
import csv
with open('Links_250.csv', 'w') as outfile:
w = csv.writer(outfile, delimiter=',', quotechar='"')
w.writerow(['Source','Target'])
f... | _____no_output_____ | Unlicense | notebooks/22_0_L_ExploratoryDataAnalysis.ipynb | luiservela/AstraZeneca |
AI SATURDAYS DONOSTIA 2020 Regresión Indicador "DeprRate" (Índice de Depresión) - Cluster 1 Proyecto Práctico Equipo FACEMOOD | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from regresion_functions import *
%load_ext autoreload
%autoreload 2 | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Conjunto de Datos con 3 Clusters | df = pd.read_csv('../processed-data/cluster3_socialmedia_data.csv', index_col=0)
df.head() | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Creación Índice de Depresión | df["DeprRate"]=(df["LowMood"]+df["LossOfInt"]+df["Hopeless"])/3
df.head() | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Nuevo Conjunto de Datos | df2 = df[["ASMU", "News", "PSMU", "Stress", "Inferior", "Concentrat", "Loneliness", "Fatigue", "DeprRate", "Sintomas_Cluster3"]]
print(df2.head())
print("No. Filas/Columnas del Conjunto de Datos: {}".format(df2.shape)) | ASMU News PSMU Stress Inferior \
Participant
115091 16.792208 15.012987 32.883117 37.441558 17.831169
131183 28.254237 11.593220 45.203390 16.898305 0.254237
438907 27.040816 34.645833 44.59... | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Datos Cluster 1 | df3=df2[df2["Sintomas_Cluster3"]==1]
df3= df3[["ASMU", "News", "PSMU", "Stress", "Inferior", "Concentrat", "Loneliness", "Fatigue", "DeprRate"]]
print(df3.head())
print("No. Filas/Columnas del Conjunto de Datos: {}".format(df3.shape)) | ASMU News PSMU Stress Inferior \
Participant
115091 16.792208 15.012987 32.883117 37.441558 17.831169
438907 27.040816 34.645833 44.595745 25.000000 23.395833
680605 1.463158 14.631579 34.54... | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Estadísticas descriptivas de las medias por participante del Cluster 1 | df3.describe() | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Diagrama de Matriz para las Medias de las 9 Variables | printMatrixDiagram(df3) # Función definida en "regresion_functions" | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Correlaciones de Pearson para las Medias de las 9 Variables | printPearsonCorrelations(df3) # Función definida en "regresion_functions" | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Se observan correlaciones más significativas entre las siguientes variables:DeprRate vs LonelinessDeprRate vs InferiorLoneliness vs InferiorNo se observa "multicolinealidad" Regresión Lineal para las Medias: y = DeprRate, X = Demás Variables | label = df3.DeprRate
df3.drop('DeprRate', axis=1, inplace=True) | _____no_output_____ | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Proceso de eliminación de variables X que no contribuyen significativamente para explicar y | resultsummary = pd.DataFrame(data={'iteration': [], 'intercept': [], 'RMSE_Training': [], 'RMSE_Testing': [],
'R2_Training': [],'R2_Testing': [],'p_value_max':[],'removed_var':[]})
data_list_medias = calculateRegression(df3, label, resultsummary, alpha=0.15) # Función definida en "... | iteration intercept RMSE_Training RMSE_Testing R2_Training R2_Testing \
0 0.0 2.680 3.461 3.232 0.803 -0.225
1 1.0 2.228 3.468 3.121 0.802 -0.143
2 2.0 2.573 3.473 3.320 0.802 -0.293... | MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Análisis de Residuos Modelo Final | fitt = data_list_medias[5]
standardized_residuals = data_list_medias[4]
residualAnalysis(fitt, standardized_residuals) # Función definida en "regresion_functions" | Estadística prueba normalidad Kolmogorov-Smirnov=0.107, pvalue=0.815
Probablemente Normal
| MIT | scripts/Regresion_Depr_Rate_Cluster_1.ipynb | henrycorazza/AISaturdays-depresion-rrss |
Collecting GPS coordinates from photo metadataFor orientation: This page is the main panel and to the left you should see a 'file browser' panel listing some folders and files, including this one. You may need to click and drag the border in between the two panes in order to better see the names. In particular you wa... | output_file = "coords.tsv"
#The following command is based on Phil Harvey's answer on January 20, 2011, 07:30:32 PM, &
# comment on on: January 20, 2011 to add the -n option, from
# http://u88.n24.queensu.ca/exiftool/forum/index.php?topic=3075.0
!exiftool -filename -gpslatitude -gpslongitude -T -n PUT_PHOTOS_HERE > {ou... | _____no_output_____ | MIT | index.ipynb | fomightez/photo2GPS |
Packages | import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
import random | _____no_output_____ | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
Initialize Data | DATADIR = "/DriveArchive1/NN_DATASETS/PetImages"
CATEGORIES = ["Dog", "Cat"]
for category in CATEGORIES: # do dogs and cats
path = os.path.join(DATADIR,category) # create path to dogs and cats
for img in os.listdir(path): # iterate over each image per dogs and cats
img_array = cv2.imread(os.path.jo... | _____no_output_____ | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
Build Training Data | training_data = []
def create_training_data():
for category in CATEGORIES: # do dogs and cats
path = os.path.join(DATADIR,category) # create path to dogs and cats
class_num = CATEGORIES.index(category) # get the classification (0 or a 1). 0=dog 1=cat
for img in tqdm(os.listdir(path)):... | 4%|▍ | 490/12501 [00:00<00:07, 1577.10it/s]Warning: unknown JFIF revision number 0.00
18%|█▊ | 2213/12501 [00:01<00:06, 1528.94it/s]Corrupt JPEG data: 226 extraneous bytes before marker 0xd9
39%|███▊ | 4835/12501 [00:03<00:05, 1501.41it/s]Corrupt JPEG data: 65 extraneous bytes before marker 0xd9... | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
**shuffle data** | random.shuffle(training_data)
for sample in training_data[:10]:
print(sample[1]) | 0
0
0
0
1
1
1
0
1
0
| 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
Make a Model | X = []
y = []
for features,label in training_data:
X.append(features)
y.append(label)
print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) | [[[[ 52]
[124]
[134]
...
[ 93]
[196]
[ 91]]
[[123]
[ 79]
[129]
...
[179]
[101]
[100]]
[[127]
[130]
[ 93]
...
[ 84]
[ 92]
[ 91]]
...
[[141]
[140]
[141]
...
[123]
[ 92]
[157]]
[[132]
[ 95]
[125]
...
[121]
[160]
[ 99]]... | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
**Export Data** | import pickle
pickle_out = open("X.pickle","wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close() | _____no_output_____ | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
**Import Data** | pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in) | _____no_output_____ | 0BSD | tensorflow_intro/part2-loading_your_own_data.ipynb | pbrainz/intro-to-ml |
Project: Identify Customer SegmentsIn this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highe... | # import libraries here; add more as necessary
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# magic word for producing visualizations in notebook
%matplotlib inline
'''
Import note: The classroom currently uses sklearn version 0.19.
If you need to use an impute, it is a... | _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 0: Load the DataThere are four files associated with this project (not including this one):- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company... | # Load in the general demographics data.
azdias =
# Load in the feature summary file.
feat_info =
# Check the structure of the data after it's loaded (e.g. print the number of
# rows and columns, print the first few rows).
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cel... | # Identify missing or unknown data values and convert them to NaNs.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 1.1.2: Assess Missing Data in Each ColumnHow much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualiz... | # Perform an assessment of how much missing data there is in each column of the
# dataset.
# Investigate patterns in the amount of missing data in each column.
# Remove the outlier columns from the dataset. (You'll perform other data
# engineering tasks such as re-encoding and imputation later.)
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 1.1.2: Assess Missing Data in Each Column(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?) Step 1.1.3: Assess Missing Data... | # How much data is missing in each row of the dataset?
# Write code to divide the data into two subsets based on the number of missing
# values in each row.
# Compare the distribution of values for at least five columns where there are
# no or few missing values, between the two subsets.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 1.1.3: Assess Missing Data in Each Row(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?) Step 1.2: Select and Re-Encode F... | # How many features are there of each data type?
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 1.2.1: Re-Encode Categorical FeaturesFor categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:- For binary (two-level) categorical that take numeric values, you can keep them without needing to do anything.- There ... | # Assess categorical variables: which are binary, which are multi-level, and
# which one needs to be re-encoded?
# Re-encode categorical variable(s) to be kept in the analysis.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 1.2.1: Re-Encode Categorical Features(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?) Step 1.2.2: Engineer Mixed-Type FeaturesTher... | # Investigate "PRAEGENDE_JUGENDJAHRE" and engineer two new variables.
# Investigate "CAMEO_INTL_2015" and engineer two new variables.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 1.2.2: Engineer Mixed-Type Features(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?) Step 1.2.3: Complete Feature SelectionIn order... | # If there are other re-engineering tasks you need to perform, make sure you
# take care of them here. (Dealing with missing data will come in step 2.1.)
# Do whatever you need to in order to ensure that the dataframe only contains
# the columns that should be passed to the algorithm functions.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 1.3: Create a Cleaning FunctionEven though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the... | def clean_data(df):
"""
Perform feature trimming, re-encoding, and engineering for demographics
data
INPUT: Demographics DataFrame
OUTPUT: Trimmed and cleaned demographics DataFrame
"""
# Put in code here to execute all main cleaning steps:
# convert missing value codes into Na... | _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 2: Feature Transformation Step 2.1: Apply Feature ScalingBefore we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll w... | # If you've not yet cleaned the dataset of all NaN values, then investigate and
# do that now.
# Apply feature scaling to the general population demographics data.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 2.1: Apply Feature Scaling(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.) Step 2.2: Perform Dimensionality ReductionOn your scaled data, you are now ready to apply dimensionality reduction techniques.- Use sklearn's [PCA](http://scikit-... | # Apply PCA to the data.
# Investigate the variance accounted for by each principal component.
# Re-apply PCA to the data while selecting for number of components to retain.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 2.2: Perform Dimensionality Reduction(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?) Step 2.3: Interpret Pri... | # Map weights for the first principal component to corresponding feature names
# and then print the linked values, sorted by weight.
# HINT: Try defining a function here or in a new cell that you can reuse in the
# other cells.
# Map weights for the second principal component to corresponding feature names
# and then... | _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 2.3: Interpret Principal Components(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?) Step 3: Clustering Step... | # Over a number of different cluster counts...
# run k-means clustering on the data and...
# compute the average within-cluster distances.
# Investigate the change in within-cluster distance across number of clusters.
# HINT: Use matplotlib's plot function to visualize this relationship.
... | _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Discussion 3.1: Apply Clustering to General Population(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?) Step 3.2: Apply All Steps to the Customer DataNow that you have cluste... | # Load in the customer demographics data.
customers =
# Apply preprocessing, feature transformation, and clustering from the general
# demographics onto the customer data, obtaining cluster predictions for the
# customer demographics data.
| _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
Step 3.3: Compare Customer Data to Demographics DataAt this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distribution... | # Compare the proportion of data in each cluster for the customer data to the
# proportion of data in each cluster for the general population.
# What kinds of people are part of a cluster that is overrepresented in the
# customer data compared to the general population?
# What kinds of people are part of a cluster ... | _____no_output_____ | CC0-1.0 | Identify_Customer_Segments.ipynb | OS-Shoaib/Customer-Segments-with-Arvato |
结巴分词 | import jieba
seg_list = jieba.cut("我来到北京清华大学")
print(' '.join(seg_list)) | Building prefix dict from the default dictionary ...
Dumping model to file cache C:\Users\Jan\AppData\Local\Temp\jieba.cache
Loading model cost 0.935 seconds.
Prefix dict has been built succesfully.
| MIT | Task 2/Task 2.ipynb | yangjiada/NLP |
自定义词典 | jieba.load_userdict("dict.txt")
import jieba.posseg as pseg
test_sent = (
"李小福是创新办主任也是云计算方面的专家; 什么是八一双鹿\n"
"例如我输入一个带“韩玉赏鉴”的标题，在自定义词库中也增加了此词为N类\n"
"「台中」正確應該不會被切開。mac上可分出「石墨烯」；此時又可以分出來凱特琳了。"
)
words = jieba.cut(test_sent)
' '.join(words) | _____no_output_____ | MIT | Task 2/Task 2.ipynb | yangjiada/NLP |
基于 TF-IDF 算法的关键词抽取 | sentence = """
《复仇者联盟4》上映16天，连续16天获得单日票房冠军，《何以为家》以优质的口碑正在冲击3亿票房，但市场大盘又再次回落至4千万元一天的水平，随着影片热度逐渐退却，靠它们“续命”的影院也重回经营窘境。
"""
import jieba.analyse
jieba.analyse.extract_tags(sentence, topK=20, withWeight=False, allowPOS=()) | _____no_output_____ | MIT | Task 2/Task 2.ipynb | yangjiada/NLP |
基于 TextRank 算法的关键词抽取 | jieba.analyse.textrank(sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v')) | _____no_output_____ | MIT | Task 2/Task 2.ipynb | yangjiada/NLP |
Copyright 2020 Google LLC. | #@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 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Point Clouds for tensorflow_graphics Run in Google Colab View source on GitHub Initialization Clone repositories, and install requirements and custom_op package | # Clone repositories
!rm -r graphics
!git clone https://github.com/schellmi42/graphics
# install requirements and load tfg module
!pip install -r graphics/requirements.txt
# install custom ops
!pip install graphics/tensorflow_graphics/projects/point_convolutions/custom_ops/pkg_builds/tf_2.2.0/*.whl
| _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Load modules | import sys
# (this is equivalent to export PYTHONPATH='$HOME/graphics:/content/graphics:$PYTHONPATH', but adds path to running session)
sys.path.append("/content/graphics")
# load point cloud module
# (this is equivalent to export PYTHONPATH='/content/graphics/tensorflow_graphics/projects/point_convolutions:$PYTHONPA... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Check if it loads without errors | import tensorflow as tf
import tensorflow_graphics as tfg
import pylib.pc as pc
import numpy as np
print('TensorFlow version: %s'%tf.__version__)
print('TensorFlow-Graphics version: %s'%tfg.__version__)
print('Point Cloud Module: ', pc) | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Example Code 2D square point clouds using segmentation IDsHere we create a batch of point clouds with variable number of points per cloud from unordered points with an additional id tensor.The `batch_ids` are the segmentation ids, which indicate which point belongs to which point cloud in the batch. For more informat... | import numpy as np
def square(num_samples, size=1):
# 2D square in 3D for easier visualization
points = np.random.rand(num_samples, 2)*2-1
return points*size
num_samples=1000
batch_size = 10
# create numpy input data consisting of points and segmentation identifiers
points = square(num_samples)
batch_ids = np... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Create a batch of point hierarchies using sequential poisson disk sampling with pooling radii 0.1, 0.4, 2. | # numpy input parameters
sampling_radii = np.array([[0.1], [0.4], [2]])
# create tensorflow point hierarchy
point_hierarchy = pc.PointHierarchy(point_cloud,
sampling_radii,
'poisson_disk')
# print information
num_levels = len(sampling_radii) + 1
p... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
assign a shape to the batch and look at the sizes again | point_hierarchy.set_batch_shape([2, 5])
print('%s point clouds of sizes:'%point_cloud._batch_size)
sizes = point_hierarchy.get_sizes()
for i in range(num_levels):
print('level: ' + str(i))
print(sizes[i].numpy()) | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Visualize the levels of one example from the batch. | import matplotlib.pyplot as plt
%matplotlib inline
# which example from the batch to choose, can be 'int' or relative in [A1,...,An]
batch_id = [0,1]
curr_points = point_hierarchy.get_points(batch_id)
# plotting
plt.figure(figsize=[num_levels*5,5])
for i in range(num_levels):
plt.subplot(1,num_levels,i+1)
plt.pl... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
3D point clouds from input files using arbitrary batch sizes with paddingHere we create point clouds from input files using a zero padded representation of shape `[A1, .., An, V, D]`.Internally this is converted to a segmented representation. Loading from ASCII .txt files | import pylib.io as io
# SHREC15
#### get files ####
input_dir = 'graphics/tensorflow_graphics/projects/point_convolutions/test_point_clouds/SHREC15/'
filenames = tf.io.gfile.listdir(input_dir)
batch_size = len(filenames)
print('### batch size ###'); print(batch_size)
for i in range(batch_size):
filenames[i] = inpu... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Loading vertices from mesh files | # Thingi10k meshes
#### get files ####
input_dir = 'graphics/tensorflow_graphics/projects/point_convolutions/test_point_clouds/meshes/'
filenames = tf.io.gfile.listdir(input_dir)
batch_size = len(filenames)
print('### batch size ###'); print(batch_size)
for i in range(batch_size):
filenames[i] = input_dir+filenames... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Monte-Carlo Convolutions Create convolutions for a point hierarchy with MLPs as kernel | import numpy as np
### create random input data
num_pts = 1000
point_dim = 3
feature_dim = 3
batch_size = 10
# create random points
points = np.random.rand(num_pts,point_dim)
batch_ids = np.random.randint(0,batch_size,num_pts)
batch_ids[:batch_size] = np.arange(0,batch_size) # ensure non-empty point clouds
# create ra... | _____no_output_____ | Apache-2.0 | tensorflow_graphics/projects/point_convolutions/pylib/notebooks/Introduction.ipynb | schellmi42/graphics |
Lab 1: Overview, Review, and Environments ObjectivesIn this lab, we'll - Review the computational infrastructure around our data science environments,- Go through the process of ensuring that we have a Python environment set up for this class with the proper installed packages- Within our environment, we'll review the... | !echo $PATH | /Users/ipasha/anaconda3/bin:/Users/ipasha/anaconda3/condabin:/anaconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Users/ipasha/anaconda3/bin:.
| MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
```{note}The "!" in my notebook allows me to run terminal commands from a notebook; you don't need this symbol when running commands in an actual terminal.``` I can check my python as follows: | !which python | /Users/ipasha/anaconda3/bin/python
| MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
We can see that calls to `python` are triggering the python installed in `anaconda3`, which is what we want (see the installation video for more details). If your call to `which python` in the terminal returns something like `usr/bin/python`, then something has likely gone wrong with your installation. There are some t... | conda create -n a330 python=3.8 | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Once you run this, answer "y" to the prompts, and your new environment will be installed.```{note}The above command may take several minutes to execute.```Next, we want to activate this environment (still in our terminal). We do this as follows: | conda activate a330 | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
When you do so, you should see the left hand edge of your prompt switch from (base) to (a330). Next, let's make an alias so that getting into our a330 environment is a snap. We're going to access a file called `.bash_profile`, which allows us to set aliases and environment variables. This file is located in your home d... | !more ~/.bash_profile | # >>> conda init >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$(CONDA_REPORT_ERRORS=false '/anaconda3/bin/conda' shell.bash hook 2> /dev/null)"
if [ $? -eq 0 ]; then
\eval "$__conda_setup"
else
if [ -f "/anaconda3/etc/profile.d/conda.sh" ]; then
# . "/anaconda3/etc/profile.d... | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Notice above I use the `~` which is a shorthand for home directory. On my computer, the default home directory for my user is `/Users/ipasha/`. This file has some conda stuff in it at the top, as well as some path and python path exports, as well as an alias. Yours should also have the conda init stuff, if you installe... | conda install -c anaconda ipykernel | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
This ensures we can select different kernels inside jupyter. A kernel is basically "the thing that is python", the root thing being run on your system when you use python. By creating environments, we're creating different unique kernels, and we can now get to them within our notebooks. Now, run the following: | python -m ipykernel install --user --name=a330 | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Once you've done this, you should have the ability to access your new environment from within Jupyter. We can test this as follows: - First, open a new terminal window, and activate your environment (if you made the alias, this means typing `a330` in your terminal. - Next, type `jupyter lab` to open jupyter lab. If for... | conda install -n a330 numpy scipy astropy matplotlib | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
(again, hitting "y" when prompted). Again, this step might take a minute or so to run.Congrats, you now have an environment set up for this class, and can jump in and out of it at will, either in your terminal, or within a Jupyter notebook.```{admonition} Hot TipIt's highly recommended you do these steps anytime you st... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Question 2The distribution of pixels in your above image should not have many outliers beyond 3-sigma from the mean, but there will be some. Find the location of any 3-sigma outliers in the image, and highlight them by circling their location. Confirm that the fraction of these out of the total number of pixels agrees... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Question 3When dealing with astronomical data, it is sometimes advisable to not include outliers in a calculation being performed on a set of data (in this example, an image). We know, of course, that the data we're plotting ARE coming from a gaussian distribution, so there's no reason to exclude, e.g., 3-sigma outlie... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Clipping the outliers of this distribution should not affect the mean in any strong way, but should noticably decrease $\sigma$. Question 4:Using Array indexing, re-plot the same array from above, but zoom in on the inner 20% of the image, such that the full width is 20% of the total. Note: try not to hard code your ... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Your image should now be 200 by 200 pixels across. Note that your new image has its own indexing. A common "gotcha" when working with arrays like this is to index in, but then try to use indices found (e.g., via `where()`) in the larger array on the cropped in version, which can lead to errors. Question 5Often, we hav... | total = 0
for i in a:
for j in b:
total+= i*j | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
which, mathematically, makes sense! But as it turns out, there's a way we can do this without any loops at all --- and when $\vec{a}$ and $\vec{b}$ get long, this becomes hugely important in our code.The trick we're going to use here is called [array broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.h... | a = np.array([1,5,10,20])
b = np.array([1,2,4,16])
# Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
```{tip}If you're familiar with the jupyter magic command `%%timeit`, try timing your loop vs non-loop solutions with a longer list (say, 5000 random numbers in $\vec{a}$ and $\vec{b}$). How much faster is the non-loop?``` Question 6Often in astronomy we need to work with grids of values. For example, let's say we hav... | def chi2(a,b):
return ((15-a)**2+(12-b)**2)**0.2 #note, this is nonsense, but should return a different value for each input a,b
# Your Code
| _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Question 7 Re-show your final plot above, making the following changes:- label your colorbar as $\chi^2$ using latex notation, with a fontsize>13- Make your ticks point inward and be longer- Make your ticks appear on the top and right hand axes of the plot as well - If you didn't already, label the x and y axes approp... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Question 8Some quick list comprehensions! For any unfamilar, **comprehensions** are pythonic statements that allow you to compress a for-loop (generally) into a single line, and usually runs faster than a full loop (but not by a ton). Take the for-loop below and write it as a list comprehension. | visited_cities = ['San Diego', 'Boston', 'New York City','Atlanta']
all_cities = ['San Diego', 'Denver', 'Boston', 'Portland', 'New York City', 'San Francisco', 'Atlanta']
not_visited = []
for city in all_cities:
if city not in visited_cities:
not_visited.append(city)
print(not_visited)
# Your Cod... | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Next, create an array of integers including 1 through 30, inclusive. Using a comprehension, create a numpy array containing the squared value of only the odd numbers in your original array. (*Hint, remember the modulo operator*) | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
In the next example, you have a list of first names and a list of last names. Use a list comprehension to create an array that is a list of full names (with a space between first and last names). | first_names = ['Bob','Samantha','John','Renee']
last_names = ['Smith','Bee','Oliver','Carpenter']
# Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
```{admonition} Challenge Problem (worth Extra Credit) I've created new lists that contain strings of the names in the format Lastname,Firstname, with random leading/trailing spaces and terrible capitalizations. Use a list comprehension to make our nice, "Firstname Lastname" list again.``` | all_names = ['sMitH,BoB ', ' bee,samanthA',' oLIVER,JOHN ',' caRPENTer,reneE ']
# Your Code
| _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
```{note}Note that with this last example, we're entering a degree of single-line length and complexity that it almost doesn't make sense to use a comprehension anymore. Just because something CAN be done in one line doesn't mean is has to be, or should be.```You may be wondering what use case this type of coding has i... | XX = np.array([1,2,3,4,5,6,7,8,9])
YY = np.array([5,6,7,8,9,10,11,12,13])
ZZ = np.array([10,11,12,13,14,15,16,17,18])
# Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Question 10 Units, units, units. The bane of every scientists' existence... except theorists that set every constant equal to 1. In the real world, we measure fluxes or magnitudes in astronomical images, infer temperatures and densities from data and simulations, and ultimately have to deal with units one way or anoth... | import astropy.units as u | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
The standard import for this library is `u`, so be careful not to name any variables that letter. To "assign" units to a variable, we multiply by the desired unit as follows. Note that generally the module knows several aliases/common abrreviations for a unit, if it is uniquely identifiable. | star_temp = 5000*u.K
star_radius = 0.89 * u.Rsun
star_mass = 0.6 * u.Msun | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
We can perform trivial conversions using the `.to()` method. | star_radius.to(u.km) | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Once we attach units to something, it is now a `Quantity` object. Quantity objects are great, above, we saw they have built-in methods to facilitate conversion. They can also be annoying -- sometimes another function we've written needs just the raw value or array back out. To get this, we use the `.value` attribute of... | star_mass.to(u.kg).value | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
This now strips away all `Quantity` stuff and gives us an array or value to use elsewhere in our code. Units are great because they help us combine quantities while tracking units and dimensional analysis. A common operation in astronomy is converting a flux to a luminosity given a distance, using $$F = \frac{L}{4\pi D... | L = 4 * np.pi * (3.6*u.Mpc)**2 * (7.5e-14 * u.erg/u.s/u.cm**2)
L.to(u.Lsun) | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
This conversion worked because the units worked out. If my units of flux weren't correct, I'd get an error: | L = 4 * np.pi * (3.6*u.Mpc)**2 * (7.5e-14 * u.erg/u.s/u.cm**2/u.AA)
L.to(u.Lsun) | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
Here, `units` realized that I was putting in units of flux density, but wanted a luminosity out, and ultimately those units don't resolve out. Thus, it can be a great way to catch errors in your inputs. Note: just be careful that sometimes, you throw a constant into an equation but the constant has some units. If you'r... | # Your Code | _____no_output_____ | MIT | _build/jupyter_execute/Lab1/Lab1.ipynb | Astro-330/Astro-330.github.io |
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