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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To get asked dates binary list | import datetime
true_false_female=[]
for key in data_dose1.columns[:-1]:
changed_key=key.split('.')[0].split('/')
true_false_female.append(datetime.date(int(changed_key[2]),int(changed_key[1]),int(changed_key[0]))<datetime.date(2021,8,15))
true_false_female.append(False)
true_false_male=[]
for key in data... | _____no_output_____ | MIT | Q7_Asgn1.nbconvert.ipynb | sunil-dhaka/india-covid19-cases-and-vaccination-analysis |
Districts | districtids=[]
stateids=[]
ratio=[]
count1=[]
count2=[]
for i in range(len(district_ids)):
for j in range(data_dose1.shape[0]):
if district_ids[i]==data_dose1['District'][j+1]: # why there is 'j+1 'in this line :: due to that NaN in first raw
districtids.append(district_ids[i])
state... | _____no_output_____ | MIT | Q7_Asgn1.nbconvert.ipynb | sunil-dhaka/india-covid19-cases-and-vaccination-analysis |
States | ratio_df1=pd.DataFrame({'districtid':districtids,'covaxin':count2,'covishield':count1,'stateid':stateids})
unique_state_codes=np.array(np.unique(stateids))
stateid=[]
ratio_state=[]
covaxin_count=[]
covishield_count=[]
for i in range(len(unique_state_codes)):
stateid.append(unique_state_codes[i])
foo_df=ratio_d... | _____no_output_____ | MIT | Q7_Asgn1.nbconvert.ipynb | sunil-dhaka/india-covid19-cases-and-vaccination-analysis |
Overall | # overall
overall_df=pd.DataFrame({'overallid':['IN'], 'vaccinationratio':[np.round(sum(covishield_count)/sum(covaxin_count),3)]})
overall_df.to_csv('overall-vaccine-type-ratio.csv',index=False) | _____no_output_____ | MIT | Q7_Asgn1.nbconvert.ipynb | sunil-dhaka/india-covid19-cases-and-vaccination-analysis |
Downloading MEDLINE/PubMed Data and Posting to PostgreSQL Brandon L. Kramer - University of Virginia's Bicomplexity Institute This notebook detail the process of downloading all of [PubMed's MEDLINE data](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) and posting it to a PostgresSQL database ([UV... | cd /scratch/kb7hp/pubmed_new
wget --recursive --no-parent ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Step 2: Download PubMedPortableSecond, we will clone [PubMedPortable package from GitHub](https://github.com/KerstenDoering/PubMedPortable). | cd /home/kb7hp/git/
git clone https://github.com/KerstenDoering/PubMedPortable.git
cd PubMedPortable | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Step 3: Populate Tables in PostgreSQL Database Go to the [PubMedPortable](https://github.com/KerstenDoering/PubMedPortable/wikibuild-up-a-relational-database-in-postgresql) protocol: - Skip the part on making a superuser named parser and use Rivanna login and pwd instead - Since `PubMedPortable` is written with the... | psql -U login -d sdad -h postgis1
CREATE SCHEMA pubmed_2021; | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Then return to the Python terminal and run this to populate the new schema: | cd /home/kb7hp/git/PubMedPortable
python PubMedDB.py -d pubmed_2021 | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Go back to the Rivanna PostgreSQL shell to check if that worked: | \dt pubmed_2021.* | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Looks like it did so now we can start parsing. Step 4: Testing MEDLINE Data Upload We don't want to start dumping all 1062 files, so let's just start with one. We will create a pm_0001 folder and download just one of the .xml files from PubMed. Next, we had to debug the `PubMedParser.py` file by updating all of the `co... | cd /home/kb7hp/git/PubMedPortable/data
mkdir pm_0001
cd pm_0001
wget ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed21n0001.xml.gz
cd /home/kb7hp/git/PubMedPortable/
python PubMedParser.py -i data/pm_0001/ | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
It took about 8 minutes to run this one file. Step 5: Uploading the Rest of the MEDLINE Dataset to PostgreSQL Database in Batches Let's add the rest of the data to Postgres. Ideally, we would just dump the whole thing at once, but Rivanna limits the amount of data we can store locally (for some reason `PubMedPortable`... | # move all the .xml.gz files to their own folder
cd /scratch/kb7hp/
mkdir pubmed_gz
cd /scratch/kb7hp/pubmed_new/ftp.ncbi.nlm.nih.gov/pubmed/baseline/
mv *.gz /scratch/kb7hp/pubmed_gz
# and copy 10 of those files to that new folder
cd /scratch/kb7hp/pubmed_gz/
cp pubmed21n{0002..0011}.xml.gz /home/kb7hp/git/PubMedPor... | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
While I intially thought this process would take ~80 minutes, running these 10 files only look ~22 minutes because of the 4 cores that essentially cut the timing by a quarter. Thus, we spun an instance with 5 cores (1 extra as directed by the Rivanna admins) and ran the next ~90 files with this new allocation. When I c... | cd /scratch/kb7hp/pubmed_gz/
cp pubmed21n{0012..0100}.xml.gz /home/kb7hp/git/PubMedPortable/data/pm_0012_0100
cd /home/kb7hp/git/PubMedPortable/data/
python PubMedParser.py -i data/pm_0012_0100/ -c -p 4 | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
And indeed it did! We have loaded the first 100 files and it took just over 3 hours (13:19:19-16:22:52). Batch 4 (0101-0500)Now, let's get a bit more ambitious. Given its now night time, we are can boost the allocation to 9 cores and try ~400 files. This should take around around 7 hours to complete (400 files * 8 min... | # first we will clean up the local directory
cd /home/kb7hp/git/PubMedPortable/data/
rm -r pm_0001
rm -r pm_0002_0011
rm -r pm_0012_0100
# copy over our new files
cd /scratch/kb7hp/pubmed_gz
cp pubmed21n{0101..0500}.xml.gz /home/kb7hp/git/PubMedPortable/data/pm_0101_0500
# and then run the script for the next 400... | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
After parsing the pm_101_500 files, I woke up to a minor error, but it looks like the program continued running up through the very last citation of the last file. I checked the `pubmed_2021.tbl_abstract` table and had 6,388,959 entries while `pubmed_2021.tbl_medline_citation` had 13,095,000, which almost half of the 2... | cd /home/kb7hp/git/PubMedPortable/data
rm -r pm_0101_0500
mkdir pm_0501_0750
cd /scratch/kb7hp/pubmed_gz
cp pubmed21n{0501..0750}.xml.gz /home/kb7hp/git/PubMedPortable/data/pm_0501_0750
cd /home/kb7hp/git/PubMedPortable/
python PubMedParser.py -i data/pm_0501_0750/ -c -p 8 | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
This took just over 4 hours (08:34:23-13:00:31) and worked flawlessly (no errors whatsoever). At this point, we have 12,158,748 abstracts in the `pubmed_2021.tbl_abstract` table. Batch 6 (0751-0900)While I thought this would be the last batch, I ran out of space again trying to dump 750-1062. Let's do up to 900 and do... | cd /home/kb7hp/git/PubMedPortable/data
rm -r pm_0501_0750
mkdir pm_0751_0900
cd /scratch/kb7hp/pubmed_gz
cp pubmed21n{0751..0900}.xml.gz /home/kb7hp/git/PubMedPortable/data/pm_0751_0900
cd /home/kb7hp/git/PubMedPortable/
python PubMedParser.py -i data/pm_0751_0900/ -c -p 8 | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
That took __ hours and once again ran without errors. Batch 7 (0901-1062)We dumped the last batch with this code and we were done! | cd /home/kb7hp/git/PubMedPortable/data
rm -r pm_0751_0900
mkdir pm_0901_1062
cd /scratch/kb7hp/pubmed_gz
cp pubmed21n{0901..1062}.xml.gz /home/kb7hp/git/PubMedPortable/data/pm_0901_1062
cd /home/kb7hp/git/PubMedPortable/
python PubMedParser.py -i data/pm_0901_1062/ -c -p 8
# started this around 8:50am | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
On to the Next Step in Your Research ProjectOverall, this was a surprisingly easy process. A major kudos goes out to PubMedPortable for this fantastic package. Now, let's get to text mining! References Döring, K., Grüning, B. A., Telukunta, K. K., Thomas, P., & Günther, S. (2016). PubMedPortable: a framework for sup... | SELECT xml_file_name, COUNT(fk_pmid)
FROM pubmed_2021.tbl_pmids_in_file
GROUP BY xml_file_name;
--- looks like there was some kind of problem parsing these files
--- affected 0816, 0829, 0865, 0866, 0875, 0879, 0884, 0886, 0891
--- all of the rest were in the high 29,000s or at 30000
--- i think i parse 900:1062 an... | _____no_output_____ | MIT | src/01_pubmed_db/.ipynb_checkpoints/02_pubmed_parser-checkpoint.ipynb | brandonleekramer/the-growth-of-diversity |
Homework 2**Instructions:** Complete the notebook below. Download the completed notebook in HTML format. Upload assignment using Canvas.**Due:** Jan. 19 at **2pm**. Exercise: NumPy ArraysFollow the instructions in the following cells. | # Import numpy
# Use the 'np.arange()' function to create a variable called 'numbers1' that stores the integers
# 1 through (and including) 10
# Print the value of 'numbers1'
# Use the 'np.arange()' function to create a variable called 'numbers2' that stores the numbers
# 0 through (and including) 1 with a step i... | _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Exercise: Random NumbersFollow the instructions in the following cells. | # Set the seed of NumPy's random number generator to 126
# Create a variable called 'epsilon' that is an array containing 25 draws from
# a normal distribution with mean 4 and standard deviation 2
# Print the value of epsilon
# Print the mean of 'epsilon'
# Print the standard deviation of 'epsilon'
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Exercise: The Cobb-Douglas Production FunctionThe Cobb-Douglas production function can be written in per worker terms as : \begin{align} y & = A k^{\alpha}, \end{align}where $y$ denotes output per worker, $k$ denotes capital per worker, and $A$ denotes total factor productivity or technology. Part (a)On a single a... | # Import the pyplot module from Matplotlib as plt
# Select the Matlplotlib style sheet to use (Optional)
# Use the '%matplotlib inline' magic command to ensure that Matplotlib plots are displayed in the Notebook
# Set capital share (alpha)
# Create an array of capital values
# Plot production function for each... | _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
**Question**1. *Briefly* explain in words how increasing $A$ affects the shape of the production function. **Answer**1. Part (b)On a single axis: plot the Cobb-Douglas production for $\alpha$ = 0.1, 0.2, 0.3, 0.4, and 0.5 with $A$ = 1 and $k$ ranging from 0 to 10. Each line should have a different color. Your plot m... | # Set TFP (A)
# Plot production function for each of the given values for alpha
# Add x- and y-axis labels
# Add a title to the plot
# Create a legend
# Add a grid
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
**Question**1. *Briefly* explain in words how increasing $\alpha$ affects the shape of the production function. **Answer**1. Exercise: The CardioidThe cardioid is a shape described by the parametric equations: \begin{align} x & = a(2\cos \theta - \cos 2\theta), \\ y & = a(2\sin \theta - \sin 2\theta). \end{align... | # Construct data for x and y
# Plot y against x
# Create x-axis label
# Create y-axis label
# Create title for plot
# Add a grid to the plot
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Exercise: Unconstrained optimizationConsider the quadratic function: \begin{align}f(x) & = -7x^2 + 930x + 30\end{align} You will use analytic (i.e., pencil and paper) and numerical methods to find the the value of $x$ that maximizes $f(x)$. Another name for $x$ that maximizes $f(x)$ is the *argument of the maxim... | # Use np.arange to create a variable called 'x' that is equal to the numbers 0 through 100
# with a spacing between numbers of 0.1
# Create a variable called 'f' that equals f(x) at each value of the array 'x' just defined
# Use np.argmax to create a variable called xstar equal to the value of 'x' that maximizes t... | _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Part (c): EvaluationProvide answers to the follow questions in the next cell.**Questions**1. How did the choice of step size in the array `x` affect the accuracy of the computed results in the first two cells of Part (b)?2. What do you think is the drawback to decreasing the stepsize in `x`?3. In the previous cell, wh... | # Assign values to the constants alpha, beta, M, px, py
# Create an array of x values
# Create an array of utility values
# Plot utility against x.
# x- and y-axis labels
# Title
# Add grid
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Part (b)The NumPy function `np.max()` returns the highest value in an array and `np.argmax()` returns the index of the highest value. Print the highest value and index of the highest value of `utility`. | _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 | |
Part (c)Use the index of the highest value of utility to find the value in `x` with the same index and store value in a new variable called `xstar`. Print the value of `xstar`. | # Create variable 'xstar' equal to value in 'x' that maximizes utility
# Print value of 'xstar'
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Part (d)Use the budget constraint to the find the implied utility-maximizing vaue of $y$ and store this in a variable called `ystar`. Print `ystar`. | # Create variable 'ystar' equal to value in 'y' that maximizes utility
# Print value of 'xstar'
| _____no_output_____ | MIT | Homework Notebooks/Econ126_Winter2021_Homework_02_blank.ipynb | letsgoexploring/econ126 |
Use Spark to recommend mitigation for car rental company with `ibm-watson-machine-learning` This notebook contains steps and code to create a predictive model, and deploy it on WML. This notebook introduces commands for pipeline creation, model training, model persistance to Watson Machine Learning repository, model d... | api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'
wml_credentials = {
"apikey": api_key,
"url": 'https://' + location + '.ml.cloud.ibm.com'
} | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Install and import the `ibm-watson-machine-learning` package**Note:** `ibm-watson-machine-learning` documentation can be found here. | !pip install -U ibm-watson-machine-learning
from ibm_watson_machine_learning import APIClient
client = APIClient(wml_credentials) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Working with spacesFirst of all, you need to create a space that will be used for your work. If you do not have space already created, you can use [Deployment Spaces Dashboard](https://dataplatform.cloud.ibm.com/ml-runtime/spaces?context=cpdaas) to create one.- Click New Deployment Space- Create an empty space- Select... | space_id = 'PASTE YOUR SPACE ID HERE' | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
You can use `list` method to print all existing spaces. | client.spaces.list(limit=10) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
To be able to interact with all resources available in Watson Machine Learning, you need to set **space** which you will be using. | client.set.default_space(space_id) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
**Note**: Please restart the kernel (Kernel -> Restart) Test Spark | try:
from pyspark.sql import SparkSession
except:
print('Error: Spark runtime is missing. If you are using Watson Studio change the notebook runtime to Spark.')
raise | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
2. Load and explore data In this section you will load the data as an Apache Spark DataFrame and perform a basic exploration. Read data into Spark DataFrame from DB2 database and show sample record. Load data | import os
from wget import download
sample_dir = 'spark_sample_model'
if not os.path.isdir(sample_dir):
os.mkdir(sample_dir)
filename = os.path.join(sample_dir, 'car_rental_training_data.csv')
if not os.path.isfile(filename):
filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Explore data | df_data.printSchema() | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
As you can see, the data contains eleven fields. `Action` field is the one you would like to predict using feedback data in `Customer_Service` field. | print("Number of records: " + str(df_data.count())) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
As you can see, the data set contains 243 records. | df_data.select('Business_area').groupBy('Business_area').count().show()
df_data.select('Action').groupBy('Action').count().show(truncate=False) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
3. Create an Apache Spark machine learning modelIn this section you will learn how to:- [3.1 Prepare data for training a model](prep)- [3.2 Create an Apache Spark machine learning pipeline](pipe)- [3.3 Train a model](train) 3.1 Prepare data for training a modelIn this subsection you will split your data into: train a... | train_data, test_data = df_data.randomSplit([0.8, 0.2], 24)
print("Number of training records: " + str(train_data.count()))
print("Number of testing records : " + str(test_data.count())) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
3.2 Create the pipeline In this section you will create an Apache Spark machine learning pipeline and then train the model. | from pyspark.ml.feature import OneHotEncoder, StringIndexer, IndexToString, VectorAssembler, HashingTF, IDF, Tokenizer
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml import Pipeline, Model | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
In the following step, use the StringIndexer transformer to convert all the string fields to numeric ones. | string_indexer_gender = StringIndexer(inputCol="Gender", outputCol="gender_ix")
string_indexer_customer_status = StringIndexer(inputCol="Customer_Status", outputCol="customer_status_ix")
string_indexer_status = StringIndexer(inputCol="Status", outputCol="status_ix")
string_indexer_owner = StringIndexer(inputCol="Car_Ow... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
4. Persist model In this section you will learn how to store your pipeline and model in Watson Machine Learning repository by using python client libraries. **Note**: Apache® Spark 2.4 is required. Save training data in your Cloud Object Storage ibm-cos-sdk library allows Python developers to manage Cloud Object Stor... | import ibm_boto3
from ibm_botocore.client import Config | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
**Action**: Put credentials from Object Storage Service in Bluemix here. | cos_credentials = {
"apikey": "***",
"cos_hmac_keys": {
"access_key_id": "***",
"secret_access_key": "***"
},
"endpoints": "***",
"iam_apikey_description": "***",
"iam_apik... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
**Action**: Define the service endpoint we will use. **Tip**: You can find this information in Endpoints section of your Cloud Object Storage intance's dashbord. | service_endpoint = 'https://s3.us.cloud-object-storage.appdomain.cloud' | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
You also need IBM Cloud authorization endpoint to be able to create COS resource object. | auth_endpoint = 'https://iam.cloud.ibm.com/identity/token' | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
We create COS resource to be able to write data to Cloud Object Storage. | cos = ibm_boto3.resource('s3',
ibm_api_key_id=cos_credentials['apikey'],
ibm_service_instance_id=cos_credentials['resource_instance_id'],
ibm_auth_endpoint=auth_endpoint,
config=Config(signature_version='oauth'),
... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Now you will create bucket in COS and copy `training dataset` for model from **car_rental_training_data.csv**. | from uuid import uuid4
bucket_uid = str(uuid4())
score_filename = "car_rental_training_data.csv"
buckets = ["car-rental-" + bucket_uid]
for bucket in buckets:
if not cos.Bucket(bucket) in cos.buckets.all():
print('Creating bucket "{}"...'.format(bucket))
try:
cos.create_bucket(Bucket=b... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Create connections to a COS bucket | datasource_type = client.connections.get_datasource_type_uid_by_name('bluemixcloudobjectstorage')
conn_meta_props= {
client.connections.ConfigurationMetaNames.NAME: "COS connection - spark",
client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: datasource_type,
client.connections.ConfigurationMetaName... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
**Note**: The above connection can be initialized alternatively with `api_key` and `resource_instance_id`. The above cell can be replaced with:```conn_meta_props= { client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ", client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: c... | connection_id = client.connections.get_uid(conn_details) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
4.2 Save the pipeline and model | training_data_references = [
{
"id":"car-rental-training",
"type": "connection_asset",
"connection": {
"id": connection_id
},
"location": {
"bucket": bucket... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Get saved model metadata from Watson Machine Learning. | published_model_id = client.repository.get_model_uid(saved_model)
print("Model Id: " + str(published_model_id)) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
**Model Id** can be used to retrive latest model version from Watson Machine Learning instance. Below you can see stored model details. | client.repository.get_model_details(published_model_id) | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
5. Deploy model in the IBM Cloud You can use following command to create online deployment in cloud. | deployment_details = client.deployments.create(
published_model_id,
meta_props={
client.deployments.ConfigurationMetaNames.NAME: "CARS4U - Action Recommendation model deployment",
client.deployments.ConfigurationMetaNames.ONLINE: {}
}
)
deployment_details | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
6. Score | fields = ['ID', 'Gender', 'Status', 'Children', 'Age', 'Customer_Status','Car_Owner', 'Customer_Service', 'Business_Area', 'Satisfaction']
values = [3785, 'Male', 'S', 1, 17, 'Inactive', 'Yes', 'The car should have been brought to us instead of us trying to find it in the lot.', 'Product: Information', 0]
import json
... | _____no_output_____ | Apache-2.0 | cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb | muthukumarbala07/watson-machine-learning-samples |
Check If All Class 1 Bad Pixels Are Indeed Just Noisy Pixels--- | quirks_store[classes_store == 1].shape
fig = figure()#figsize=(6,6))
ax = fig.add_subplot(111)
# ax.plot([nan,nan])
corrections = []
for cnow in np.where(classes_store == 1)[0]:
# ax.lines.pop()
ax.clear()
ax.plot(quirks_store[cnow] - median(quirks_store[cnow]))
ax.set_title('Entry:' + str(cnow) + '/ C... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Check If All Class 4 Bad Pixels Are Indeed Just CR Pixels--- | quirks_store[classes_store == 4].shape | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
fig = figure()figsize=(6,6))ax = fig.add_subplot(111)CRs = np.where(classes_store == 4)[0]corrections = []for cnow in : ax.lines.pop() ax = fig.add_subplot(111) ax.plot((quirks_store[cnow] - min(quirks_store[cnow])) / (max(quirks_store[cnow]) - min(quirks_store[cnow])), lw=2) ax.set_title('Entry:' + str(... | np.where(classes_store == 6)[0] | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
classes_store[[140,260, 380]] = 2 | plot(quirks_store[140]);
plot(quirks_store[260]);
plot(quirks_store[380]);
((quirks_store.T - np.min(quirks_store,axis=1)) / (np.max(quirks_store,axis=1) - np.min(quirks_store, axis=1))).shape
((quirks_store.T - np.min(quirks_store,axis=1)) / (np.max(quirks_store,axis=1) - np.min(quirks_store, axis=1))).T[classes_store... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
classNow = 5stepsize = 50fig = figure(figsize=(16,30))for k in range( np.sum(classes_store == classNow) // stepsize): quirksNow = quirk_store_norm[classes_store == classNow][k*stepsize:(k+1)*stepsize] upper = np.where(quirksNow[:,-1] > 0.5)[0] lower = np.where(quirksNow[:,-1] < 0.5)[0] classes_st... | fig = figure(figsize=(16,8))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.plot(quirk_store_norm[classes_store == 5].T, lw=1);
ylims = ax1.get_ylim()
xlims = ax1.get_xlim()
xyNow = [np.min(xlims) + 0.5*diff(xlims),
np.min(ylims) + 0.5*diff(ylims)]
ax1.annotate(str(5), xyNow, fontsize=75)
ax2.plot... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
classes_store_new = np.copy(classes_store)classes_store_new[(classes_store == 5)*(quirk_store_norm[:,-1] < 0.5)] = 6 classes_store_new[(classes_store == 5)*(quirk_store_norm[:,-1] >= 0.5)] = classes_store[(classes_store == 5)*(quirk_store_norm[:,-1] >= 0.5)]classes_store_new[classes_store_new == 6]np.savetxt('myclasse... | darks.shape
darks_trnspsd = np.transpose(darks, axes=(1,2,0))
for irow in range(len(quirks_store)):
quirk_pp = pp.scale(quirks_store[irow])
# print(std(quirk_pp), scale.mad(quirk_pp))
plot(quirk_pp, alpha=0.5)# - median(darks_trnspsd[icol,irow])))
# darks_scaled = pp.scale(darks,axis=0)
darks.shape, darks... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
darks_flat = darks_trnspsd[darks_std < 2*darks_med_std]nNormals = len(darks_flat)nNormals | darks_norm_trnspsd = np.transpose(darks_norm, axes=(1,2,0))
darks_norm_flat = darks_norm_trnspsd[darks_std < 2*darks_med_std]
darks_norm_flat.shape
darks_norm_flat.shape[0] | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Simulate RTNs because the CV3 training data has None--- | np.random.seed(42)
saturation = 2**16
dynRange = 2**9
nSamps = 1000
nSig = 4.0
nFrames = darks_norm_flat.shape[1]
rtn_syn = np.zeros((nSamps, nFrames))
rtn_classes = np.zeros(nSamps)
maxRTNs = np.int(0.9*nFrames)
maxWidth = 50
minWidth = 10
rtnCnt = 0
dark_inds = np.arange(darks_... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Remove Common Mode variations from frame to frame (time series)---Probably related to bias drifting | plot(darks_flat_med_axis0)
quirks_store_smooth = np.copy(quirks_store) #/ darks_flat_med_axis0
rtn_syn_smooth = np.copy(rtn_syn) #/ darks_flat_med_axis0
darks_flat_sample_smooth = np.copy(darks_flat_sample) #/ darks_flat_med_axis0
print(quirks_store_smooth.shape, rtn_syn_smooth.shape, dark... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Random Forest Classification--- Load Sci-kit Learn Libraries | from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
from sklearn.externals import joblib | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
darks_classes = np.zeros(darks_flat_sample_smooth.shape[0],dtype=int)rtn_classes = rtn_classes + 3samples_train_set = vstack([quirks_store_smooth, rtn_syn_smooth, darks_flat_sample_smooth])classes_train_set = vstack([classes_store[:,None], rtn_classes[:,None], darks_classes[:,None]])[:,0] | classes_store[np.where(classes_store > 3)] += 1
darks_classes = np.zeros(darks_flat_sample_smooth.shape[0],dtype=int)
rtn_classes = rtn_classes + 3
samples_train_set = vstack([quirks_store, rtn_syn, darks_flat_sample])
classes_train_set = vstack([classes_store[:,None], rtn_classes[:,None], darks_classes[:,Non... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
sts_inds = np.arange(samples_train_set.shape[0])unsort_sts = np.random.choice(sts_inds, sts_inds.size, replace=False) | samples_train_set_resort = shuffle(np.copy(samples_train_set), random_state=42)
classes_train_resort = shuffle(np.copy(classes_train_set), random_state=42) | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Rescaled all samples from 0 to 1 | samples_train_set_resort.shape
samples_train_set_resort_scaled = (( samples_train_set_resort.T - np.min(samples_train_set_resort,axis=1)) / \
(np.max(samples_train_set_resort,axis=1) - np.min(samples_train_set_resort,axis=1))).T
samples_train_set_resort_scaled.shape
plot(sample... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Establish Random Forest Classification- 1000 trees- OOB Score- Multiprocessing | rfc = RandomForestClassifier(n_estimators=1000, oob_score=True, n_jobs=-1, random_state=42, verbose=True) | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
rfc2 = RandomForestClassifier(n_estimators=1000, oob_score=True, n_jobs=-1, random_state=42, verbose=True) | Split Samples into 75% Train and 25% Test | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
X_train, X_test, Y_train, Y_test = train_test_split(samples_train_set_resort_scaled.T, classes_train_resort, test_size = 0.25, random_state=42) X_train.shape, X_test.shape, Y_train.shape, Y_test.shape | Shuffle Training Data Set | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
X_train, Y_train = shuffle(X_train, Y_train, random_state=42) | Train Classifier with `rfc.fit` | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
rfc.fit(X_train, Y_train) | rfc.fit(samples_train_set_resort_scaled, classes_train_resort) | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Score Classifier with Test Data Score | rfc.score(samples_train_set_resort_scaled, classes_train_resort) | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Score Classifier with Out-of-Bag Error | rfc.oob_score_ | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Save Random Forest Classifier becuse 98% is AWESOME! | joblib.dump(rfc, 'trained_RF_Classifier/random_forest_classifier_trained_on_resorted_samples_train_set_RTN_CR_HP_Other_Norm.save')
joblib.dump(dict(samples=samples_train_set_resort_scaled.T, classes=classes_train_resort), 'trained_RF_Classifier/RTN_CR_HP_Other_Norm_resorted_samples_train_set.save') | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
joblib.dump(rfc2, 'trained_RF_Classifier/random_forest_classifier_trained_full_set_on_resorted_samples_train_set_RTN_CR_HP_Other_Norm.save') | darks_reshaped.shape
step = 0
skipsize = 100
chunkNow = arange(step*darks_reshaped.shape[0]//skipsize,min((step+1)*darks_reshaped.shape[0]//skipsize, darks_reshaped.shape[0]))
darks_reshaped_chunk = darks_reshaped[chunkNow]
darks_reshaped_chunk_smooth = darks_reshaped_chunk #/ darks_flat_med_axis0
darks_resh... | _____no_output_____ | BSD-3-Clause | notebooks/RTN Classification - Active.ipynb | exowanderer/BadPixelDetector |
Getting Geo-coordinates for WSJ CollegesHere we are going to use a couple of Python tools to make a database of the Latitude / Longitude locations for the different schools contained in the report. I'm doing this to compare the speed and accuracy of the included Power BI ArcGIS maps with a hard-coding the coordinates.... | geodf.head()
import pandas as pd
wsj = pd.read_csv('wsj_data.csv')
import os
if os.path.exists('wsj_locs.csv'):
geodf = pd.read_csv('wsj_locs.csv', index_col='loc_string')
else:
geodf = pd.DataFrame()
geodf.index.name = 'loc_string'
wsj.head()
| _____no_output_____ | Apache-2.0 | Geocoding_Colleges.ipynb | stkbailey/WSJ_CollegeRankings2018 |
For each college, we're going to create a search string as if we were looking it up in Google Maps. It's important to include as much information as we have so that the location service doesn't get confused with institutions in other countries, for example. | overwrite_loc_string = None
if overwrite_loc_string:
wsj['loc_string'] = wsj.apply(lambda s: '{}, {}, USA'.format(s.college, s.city_state), axis=1)
wsj.to_csv('wsj_data.csv', encoding='utf-8', index=None)
print(wsj.loc_string[0:5])
def getCoords(search_string):
'''Takes a search term, queries Google and re... | _____no_output_____ | Apache-2.0 | Geocoding_Colleges.ipynb | stkbailey/WSJ_CollegeRankings2018 |
Global Signals in Time Series DataBy Abigail Stevens Problem 1: Timmer and Koenig algorithm The algorithm outlined in Timmer & Koenig 1995 lets you define the shape of your power spectrum (a power law with some slope, a Lorentzian, a sum of a couple Lorentzians and a power law, etc.) then generate the random phases a... | n_bins = 8192 ## number of total frequency bins in a FT segment; same as number of time bins in the light curve
dt = 1./16. # time resolution of the output light curve
df = 1. / dt / n_bins | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
1a. Make an array of Fourier frequenciesYes you can do this with scipy, but the order of frequencies in a T&K power spectrum is different than what you'd get by default from a standard FFT of a light curve.You want the zero frequency to be in the middle (at index n_bins/2) of the frequency array. The positive frequenc... | #freq = fftpack.fftfreq(n_bins, d=df)
freqs = np.arange(float(-n_bins/2)+1, float(n_bins/2)+1) * df
pos_freq = freqs[np.where(freqs >= 0)]
## Positive should have 2 more than negative,
## because of the 0 freq and the nyquist freq
neg_freq = freqs[np.where(freqs < 0)]
nyquist = pos_freq[-1]
len_pos = len(pos_freq) | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
1b. Define a Lorentzian function and power law function for the shape of the power spectrum | def lorentzian(v, v_0, gamma):
""" Gives a Lorentzian centered on v_0 with a FWHM of gamma """
numerator = gamma / (np.pi * 2.0)
denominator = (v - v_0) ** 2 + (1.0/2.0 * gamma) ** 2
L = numerator / denominator
return L
def powerlaw(v, beta):
"""Gives a powerlaw of (1/v)^-beta """
pl = np.z... | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
Now the T&K algorithm. I've transcribed the 'recipe' section of the T&K95 paper, which you will convert to lines of code. 1c. Choose a power spectrum $S(\nu)$. We will use a sum of one Lorentzians (a QPO with a centroid frequency of 0.5 Hz and a FWHM of 0.01 Hz), and a Poisson-noise power law. The QPO should be 100 ... | power_shape = 100 * lorentzian(pos_freq, 0.5, 0.01) + powerlaw(pos_freq, 0) | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
1d. For each Fourier frequency $\nu_i$ draw two gaussian-distributed random numbers, multiply them by $$\sqrt{\frac{1}{2}S(\nu_i)}$$ and use the result as the real and imaginary part of the Fourier transform $F$ of the desired data.In the case of an even number of data points, for reason of symmetry $F(\nu_{Nyquist})... | from numpy.random import randn
np.random.seed(3)
rand_r = np.random.standard_normal(len_pos)
rand_i = np.random.standard_normal(len_pos-1)
rand_i = np.append(rand_i, 0.0) # because the nyquist frequency should only have a real value
## Creating the real and imaginary values from the lists of random numbers and the fre... | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
1e. To obtain a real valued time series, choose the Fourier components for the negative frequencies according to $F(-\nu_i)=F*(\nu_i)$ where the asterisk denotes complex conjugation. Append to make one fourier transform array. Check that your T&K fourier transform has length `n_bins`. Again, for this algorithm, the ze... | FT_pos = r_values + i_values*1j
FT_neg = np.conj(FT_pos[1:-1])
FT_neg = FT_neg[::-1] ## Need to flip direction of the negative frequency FT values so that they match up correctly
FT = np.append(FT_pos, FT_neg)
| _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
1f. Obtain the time series by backward Fourier transformation of $F(\nu)$ from the frequency domain to the time domain.Note: I usually use `.real` after an iFFT to get rid of any lingering 1e-10 imaginary factors. | lc = fftpack.ifft(FT).real | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
Congratulations! 1g. Plot the power spectrum of your FT (only the positive frequencies) next to the light curve it makes. Remember: $$P(\nu_i)=|F(\nu_i)|^2$$ | fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 5))
ax1.loglog(pos_freq, np.abs(FT_pos)**2)
ax2.plot(np.linspace(0, len(lc), len(lc)), lc)
ax2.set_xlim(0, 200)
fig.show() | /Users/rmorgan/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:457: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
"matplotlib is currently using a non-GUI backend, "
| MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
You'll want to change the x scale of your light curve plot to be like 20 seconds in length, and only use the positive Fourier frequencies when plotting the power spectrum. Yay! 1h. Play around with your new-found simulation powers (haha, it's a pun!) Make more power spectra with different features -- try at least 5 or... | def gaussian(v, mean, std_dev):
"""
Gives a Gaussian with a mean of mean and a standard deviation of std_dev
FWHM = 2 * np.sqrt(2 * np.log(2))*std_dev
"""
exp_numerator = -(v - mean)**2
exp_denominator = 2 * std_dev**2
G = np.exp(exp_numerator / exp_denominator)
return G
def powerlaw_ex... | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
2. More realistic simulation with T&KNow you're able to simulate the power spectrum of a single segment of a light curve. However, as you learned this morning, we usually use multiple (~50+) segments of a light curve, take the power spectrum of each segment, and average them together. 2a. Turn the code from 1d to 1e ... | fig, ax = plt.subplots(1,1, figsize=(8,5))
ax.plot(rb_freq, rb_pow, linewidth=2.0)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel(r'Frequency (Hz)', fontproperties=font_prop)
ax.tick_params(axis='x', labelsize=16, bottom=True, top=True,
labelbottom=True, labeltop=False)
ax.tick_params(axis='y',... | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
2f. Re-do 2b through the plot above but slightly changing the power spectrum shape in each segment. Maybe you change the centroid frequency of the QPO, or the normalizing factors between the two components, or the slope of the power-law. Bonus problems: 1. Use a different definition of the Lorentzian (below) to make ... | def lorentz_q(v, v_peak, q, rms):
"""
Form of the Lorentzian function defined in terms of
peak frequency v_peak and quality factor q
q = v_peak / fwhm
with the integrated rms of the QPO as the normalizing factor.
e.g. see Pottschmidt et al. 2003, A&A, 407, 1039 for more info
"""
f_re... | _____no_output_____ | MIT | Session9/Day4/workbook_globalsignals.ipynb | rmorgan10/LSSTC-DSFP-Sessions |
oneM2M - Access ControlThis notebook demonstrates how access control to resources can be done in oneM2M.- Create an <ACP> resource with different credentials for a new originator- Create a second <AE> resource with the new access controls policy- Succeed to add a <Container> to the second ≶AE> resource- Fa... | %run init.py | _____no_output_____ | BSD-3-Clause | onem2m-05-accesscontrol.ipynb | lovele0107/oneM2M-jupyter |
Create an <ACP> ResourceAccess Control Policies are used to associate access control with credentials. They define the rules to for access control to resources. Each <ACP> resource has two sections:- **pv (Privileges)** : The actual privileges defined by this policy.- **pvs (Self-Privileges)** : This defines the... | CREATE ( # CREATE request
url,
# Request Headers
{
'X-M2M-Origin' : originator, # Set the originator
'X-M2M-RI' : '0', # Request identifier
'Accept' : 'application/json', # Response sha... | _____no_output_____ | BSD-3-Clause | onem2m-05-accesscontrol.ipynb | lovele0107/oneM2M-jupyter |
Create a second <AE> Resource with the new <ACP>We now create a new <AE> resource that uses the just created <ACP>.**This should succeed.** | CREATE ( # CREATE request
url,
# Request Headers
{
'X-M2M-Origin' : 'C', # Set the originator
'X-M2M-RI' : '0', # Request identifier
'Accept' : 'application/json', # Response shal... | _____no_output_____ | BSD-3-Clause | onem2m-05-accesscontrol.ipynb | lovele0107/oneM2M-jupyter |
Try to Create <Container> under the second <AE> ResourceWe will update a <Container> resource under the second <AE> resource with the originator of *abc:xyz*. **This should work** since this originator is allowed to send CREATE requests. | CREATE ( # CREATE request
url + '/Notebook-AE_2',
# Request Headers
{
'X-M2M-Origin' : "abcxyz", # Set the originator
'X-M2M-RI' : '0', # Request identifier
'Accept' : 'application/json', ... | _____no_output_____ | BSD-3-Clause | onem2m-05-accesscontrol.ipynb | lovele0107/oneM2M-jupyter |
Try to Update the second <AE> ResourceNow we try to update the new <AE> resource (add a *lbl* attribute) with the other originator, *abc:xyz*. **This should fail**, since the associated <ACP> doesn't allow UPDATE requests. | UPDATE ( # UPDATE request
url + '/Notebook-AE_2',
# Request Headers
{
'X-M2M-Origin' : 'abcxyz', # Set the originator
'X-M2M-RI' : '0', # Request identifier
'Accept' : 'application/json', ... | _____no_output_____ | BSD-3-Clause | onem2m-05-accesscontrol.ipynb | lovele0107/oneM2M-jupyter |
[View in Colaboratory](https://colab.research.google.com/github/JacksonIsaac/colab_notebooks/blob/master/kaggle_tgs_salt_identification.ipynb) Kaggle notebookFor *TGS Salt identification* competition:https://www.kaggle.com/c/tgs-salt-identification-challenge Setup kaggle and download dataset | !pip install kaggle
## Load Kaggle config JSON
from googleapiclient.discovery import build
import io, os
from googleapiclient.http import MediaIoBaseDownload
from google.colab import auth
auth.authenticate_user()
drive_service = build('drive', 'v3')
results = drive_service.files().list(
q="name = 'kaggle.json... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Install Dependencies | !pip install -q imageio
!pip install -q torch
!pip install -q ipywidgets
import os
import numpy as np
import imageio
import matplotlib.pyplot as plt
import pandas as pd
import torch
from torch.utils import data | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Create class for input dataset | class TGSSaltDataSet(data.Dataset):
def __init__(self, root_path, file_list):
self.root_path = root_path
self.file_list = file_list
def __len__(self):
return len(self.file_list)
def __getitem__(self, index):
file_id = self.file_list[index]
# Ima... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Load dataset csv | train_mask = pd.read_csv('train.csv')
depth = pd.read_csv('depths.csv')
train_path = './'
file_list = list(train_mask['id'].values)
dataset = TGSSaltDataSet(train_path, file_list) | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Visualize dataset | def plot2x2array(image, mask):
fig, axs = plt.subplots(1, 2)
axs[0].imshow(image)
axs[1].imshow(mask)
axs[0].grid()
axs[1].grid()
axs[0].set_title('Image')
axs[1].set_title('Mask')
for i in range(5):
image, mask = dataset[np.random.randint(0, len(dataset))]
plot2x2array(ima... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Convert RLE Mask to matrix | def rle_to_mask(rle_string, height, width):
rows, cols = height, width
try:
rle_numbers = [int(numstr) for numstr in rle_string.split(' ')]
rle_pairs = np.array(rle_numbers).reshape(-1, 2)
img = np.zeros(rows * cols, dtype=np.uint8)
for idx, length in rle_pairs:
... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Create training mask | train_mask['mask'] = train_mask['rle_mask'].apply(lambda x: rle_to_mask(x, 101, 101))
train_mask['salt_proportion'] = train_mask['mask'].apply(lambda x: salt_proportion(x)) | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
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