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Part 2. Make features Calculate the number of cell phones per person, and add this column onto your dataframe.(You've calculated correctly if you get 1.220 cell phones per person in the United States in 2017.) | df.head()
df[df['country'] == 'United States']
df.dtypes
df['cell_phones_total'].value_counts().sum()
condition = (df['country'] == 'United States') & (df['time'] == 2017)
columns = ['country', 'time', 'cell_phones_total', 'population_total']
subset = df[condition][columns]
subset.shape
subset.head()
#value = subset['cell_phones_total'] / subset['population_total']
df['cell_phones_per_person'] = df['cell_phones_total'] / df['population_total'] # better way to do this
df[(df.country=='United States') & (df.time==2017)]
value | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Modify the `geo` column to make the geo codes uppercase instead of lowercase. | df.head(1)
df['geo'] = df['geo'].str.upper()
df.head() | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Part 3. Process data Use the describe function, to describe your dataframe's numeric columns, and then its non-numeric columns.(You'll see the time period ranges from 1960 to 2017, and there are 195 unique countries represented.) | # numeric columns
df.describe()
# non-numeric columns
df.describe(exclude='number') | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
In 2017, what were the top 5 countries with the most cell phones total?Your list of countries should have these totals:| country | cell phones total ||:-------:|:-----------------:|| ? | 1,474,097,000 || ? | 1,168,902,277 || ? | 458,923,202 || ? | 395,881,000 || ? | 236,488,548 | | # This optional code formats float numbers with comma separators
pd.options.display.float_format = '{:,}'.format
condition = (df['time'] == 2017)
columns = ['country', 'cell_phones_total']
subset = df[condition][columns]
subset.head()
subset = subset.sort_values(by=['cell_phones_total'], ascending=False)
subset.head(5) | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
2017 was the first year that China had more cell phones than people.What was the first year that the USA had more cell phones than people? | condition = (df['country'] == 'United States')
columns = ['time', 'country', 'cell_phones_total', 'population_total']
subset1 = df[condition][columns]
subset1.sort_values(by='time', ascending=False).head(5)
df[(df.geo=='USA') & (df.cell_phones_per_person > 1)].time.min() # the way to get the answer via coding
# The year that USA had more cell phones than people is in 2014 when cell_phones_total = 355,500,000 vs population_total = 317,718,779 | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Part 4. Reshape data *This part is not needed to pass the sprint challenge, only to get a 3! Only work on this after completing the other sections.*Create a pivot table:- Columns: Years 2007—2017- Rows: China, India, United States, Indonesia, Brazil (order doesn't matter)- Values: Cell Phones TotalThe table's shape should be: (5, 11) | condition = subset1['country'] == ('China', 'United States', 'Indonesia', 'Brazil')
columns = ['time', 'country', 'cell_phones_total', 'population_total']
subset2 = subset1[condition][columns]
subset2
subset1.pivot_table(index='columns', columns='time', values='cell_phones_total')
# ran out of time...oh well | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Sort these 5 countries, by biggest increase in cell phones from 2007 to 2017.Which country had 935,282,277 more cell phones in 2017 versus 2007? | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
|
If you have the time and curiosity, what other questions can you ask and answer with this data? Data StorytellingIn this part of the sprint challenge you'll work with a dataset from **FiveThirtyEight's article, [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](https://fivethirtyeight.com/features/every-guest-jon-stewart-ever-had-on-the-daily-show/)**! Part 0 — Run this starter codeYou don't need to add or change anything here. Just run this cell and it loads the data for you, into a dataframe named `df`.(You can explore the data if you want, but it's not required to pass the Sprint Challenge.) | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/daily-show-guests/daily_show_guests.csv'
df = pd.read_csv(url).rename(columns={'YEAR': 'Year', 'Raw_Guest_List': 'Guest'})
def get_occupation(group):
if group in ['Acting', 'Comedy', 'Musician']:
return 'Acting, Comedy & Music'
elif group in ['Media', 'media']:
return 'Media'
elif group in ['Government', 'Politician', 'Political Aide']:
return 'Government and Politics'
else:
return 'Other'
df['Occupation'] = df['Group'].apply(get_occupation) | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Part 1 — What's the breakdown of guests’ occupations per year?For example, in 1999, what percentage of guests were actors, comedians, or musicians? What percentage were in the media? What percentage were in politics? What percentage were from another occupation?Then, what about in 2000? In 2001? And so on, up through 2015.So, **for each year of _The Daily Show_, calculate the percentage of guests from each occupation:**- Acting, Comedy & Music- Government and Politics- Media- Other Hints:You can make a crosstab. (See pandas documentation for examples, explanation, and parameters.)You'll know you've calculated correctly when the percentage of "Acting, Comedy & Music" guests is 90.36% in 1999, and 45% in 2015. | df.head()
df.describe()
df.describe(exclude='number')
df1 = pd.crosstab(df['Year'], df['Occupation'], normalize='index')
df1 | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Part 2 — Recreate this explanatory visualization: | from IPython.display import display, Image
png = 'https://fivethirtyeight.com/wp-content/uploads/2015/08/hickey-datalab-dailyshow.png'
example = Image(png, width=500)
display(example) | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
**Hints:**- You can choose any Python visualization library you want. I've verified the plot can be reproduced with matplotlib, pandas plot, or seaborn. I assume other libraries like altair or plotly would work too.- If you choose to use seaborn, you may want to upgrade the version to 0.9.0.**Expectations:** Your plot should include:- 3 lines visualizing "occupation of guests, by year." The shapes of the lines should look roughly identical to 538's example. Each line should be a different color. (But you don't need to use the _same_ colors as 538.)- Legend or labels for the lines. (But you don't need each label positioned next to its line or colored like 538.)- Title in the upper left: _"Who Got To Be On 'The Daily Show'?"_ with more visual emphasis than the subtitle. (Bolder and/or larger font.)- Subtitle underneath the title: _"Occupation of guests, by year"_**Optional Bonus Challenge:**- Give your plot polished aesthetics, with improved resemblance to the 538 example.- Any visual element not specifically mentioned in the expectations is an optional bonus. | display(example)
df2 = df1.drop(['Other'], axis=1)
df2
display(example)
plt.style.use('fivethirtyeight')
yax = ['0', '25', '50', '75', '100%']
ax = df2.plot()
ax.patch.set_alpha(0.1)
plt.title("Who Got To Be On 'The Daily Show'?",
fontsize=18,
x=-0.1,
y=1.1,loc='left',
fontweight='bold');
subtitle_string = 'Occupation of guests, by year'
plt.suptitle(subtitle_string, fontsize=14, x= 0.24, y=0.94)
plt.xlabel(' ')
plt.legend(bbox_to_anchor = [0.6, 0.75]); | _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Example by Alex | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
x = np.arange(0,10)
x
y = x**2
y
plt.plot(x,y)
y_labels = [f'{i}%' for i in y]
plt.yticks(y, y_labels);
y_labels = [f'{i}' if i!= 100 else f'{i}%' for i in range(0, 101, 10)]
# y_labels = [f'{i}' if i= 100 else f'{i}%' for i in range(0, 101, 10)]
plt.plot(x,y)
plt.yticks(range(0,101,10), y_labels);
plt.title('My plot')
plt.text(x=4, y=50, s='My text')
import pandas as pd
df = pd.DataFrame({'x':X, 'y':y})
import seaborn as sns
sns.replot()
plt.yticks(range(0,101,10), y_labels);
plt.title('My plot')
plt.text(x=4, y=50, s='My text')
| _____no_output_____ | MIT | tdu1s3.ipynb | cardstud/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
IFTTT - Trigger workflow **Tags:** ifttt automation nocode Input Import library | from naas_drivers import ifttt | _____no_output_____ | BSD-3-Clause | IFTTT/IFTTT_Trigger_workflow.ipynb | vivard/awesome-notebooks |
Variables | event = "myevent"
key = "cl9U-VaeBu1**********"
data = { "value1": "Bryan", "value2": "Helmig", "value3": 27 } | _____no_output_____ | BSD-3-Clause | IFTTT/IFTTT_Trigger_workflow.ipynb | vivard/awesome-notebooks |
Model Connect to IFTTT | result = ifttt.connect(key) | _____no_output_____ | BSD-3-Clause | IFTTT/IFTTT_Trigger_workflow.ipynb | vivard/awesome-notebooks |
Output Display result | result = ifttt.send(event, data) | _____no_output_____ | BSD-3-Clause | IFTTT/IFTTT_Trigger_workflow.ipynb | vivard/awesome-notebooks |
Please enter the correct file path | train_dir = r'C:\Users\Ryukijano\Python_notebooks\Face_ Mask_ Dataset\Train'
validation_dir = r'C:\Users\Ryukijano\Python_notebooks\Face_ Mask_ Dataset\Validation'
test_dir =r'C:\Users\Ryukijano\Python_notebooks\Face_ Mask_ Dataset\Test'
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(128, 128),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(128, 128),
batch_size=20,
class_mode='binary')
from tensorflow.keras import layers
from tensorflow.keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(128, 128, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
from tensorflow.keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=500,
epochs=20,
validation_data=validation_generator,
validation_steps=40)
model.save("model_cnn_project_P1.h5")
from tensorflow.keras import backend as K
K.clear_session()
del model
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(128, 128),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(128, 128),
batch_size=32,
class_mode='binary')
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(128, 128, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=300,
epochs=10,
validation_data=validation_generator,
validation_steps=25)
from tensorflow.keras.applications import VGG19
conv_base = VGG19(weights='imagenet',
include_top=False,
input_shape=(128, 128, 3))
from tensorflow.keras import models
from tensorflow.keras import layers
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
from tensorflow.keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
checkpoint_cb = keras.callbacks.ModelCheckpoint("CNN_Final_Project_Model-{epoch:02d}.h5")
history = model.fit_generator(
train_generator,
steps_per_epoch=300,
epochs=10,
validation_data=validation_generator,
validation_steps=25,
callbacks=[checkpoint_cb])
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(128, 128),
batch_size=32,
class_mode='binary')
model.evaluate_generator(test_generator, steps=31) | _____no_output_____ | MIT | It-can-see-you.ipynb | Ryukijano/it-can-see-you |
L15 - Model evaluation 2 (confidence intervals)---- Instructor: Dalcimar Casanova ([email protected])- Course website: https://www.dalcimar.com/disciplinas/aprendizado-de-maquina- Bibliography: based on lectures of Dr. Sebastian Raschka | import numpy as np
import matplotlib.pyplot as plt | _____no_output_____ | MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
from mlxtend.data import iris_data
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X, y = iris_data()
print(np.shape(y))
X_train_valid, X_test, y_train_valid, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
X_train, X_valid, y_train, y_valid = train_test_split(X_train_valid, y_train_valid, test_size=0.5, random_state=1)
print(np.shape(y_train))
print(np.shape(y_valid))
print(np.shape(y_test))
pip install hypopt | Requirement already satisfied: hypopt in /usr/local/lib/python3.6/dist-packages (1.0.9)
Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from hypopt) (1.19.5)
Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from hypopt) (0.22.2.post1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->hypopt) (1.0.0)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->hypopt) (1.4.1)
| MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
|
from hypopt import GridSearch
#from sklearn.model_selection import GridSearchCV
knn = KNeighborsClassifier()
param_grid = {
'n_neighbors': [2, 3, 4, 5]
}
grid = GridSearch(knn, param_grid=param_grid)
grid.fit(X_train, y_train, X_valid, y_valid) | 100%|██████████| 4/4 [00:00<00:00, 265.79it/s]
| MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
|
print(grid.param_scores)
print(grid.best_params)
print(grid.best_score)
print(grid.best_estimator_)
clf = grid.best_estimator_
from sklearn.metrics import accuracy_score
y_test_pred = clf.predict(X_test)
acc_test = accuracy_score(y_test, y_test_pred)
print(acc_test) | 0.9333333333333333
| MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
|
clf.fit(X_train_valid, y_train_valid) | _____no_output_____ | MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
|
y_test_pred = clf.predict(X_test)
acc_test = accuracy_score(y_test, y_test_pred)
print(acc_test) | 0.9777777777777777
| MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
|
Confidence interval (via normal approximation) | ci_test = 1.96 * np.sqrt((acc_test*(1-acc_test)) / y_test.shape[0])
test_lower = acc_test-ci_test
test_upper = acc_test+ci_test
print(test_lower, test_upper)
ci_test = 2.58 * np.sqrt((acc_test*(1-acc_test)) / y_test.shape[0])
test_lower = acc_test-ci_test
test_upper = acc_test+ci_test
print(test_lower, test_upper) | 0.921085060454202 1.0344704951013535
| MIT | L15_model evaluation 2/code/L15_confidence intervals holdout.ipynb | pedrogomes-dev/MA28CP-Intro-to-Machine-Learning |
InventoryThe Inventory is arguably the most important piece of nornir. Let's see how it works. To begin with the [inventory](../api/nornir/core/inventory.htmlmodule-nornir.core.inventory) is comprised of [hosts](../api/nornir/core/inventory.htmlnornir.core.inventory.Hosts), [groups](../api/nornir/core/inventory.htmlnornir.core.inventory.Groups) and [defaults](../api/nornir/core/inventory.htmlnornir.core.inventory.Defaults).In this tutorial we are using the [SimpleInventory](../api/nornir/plugins/inventory/simple.htmlnornir.plugins.inventory.simple.SimpleInventory) plugin. This inventory plugin stores all the relevant data in three files. Let’s start by checking them: | # hosts file
%highlight_file inventory/hosts.yaml | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
The hosts file is basically a map where the outermost key is the name of the host and then a `Host` object. You can see the schema of the object by executing: | from nornir.core.inventory import Host
import json
print(json.dumps(Host.schema(), indent=4)) | {
"name": "str",
"connection_options": {
"$connection_type": {
"extras": {
"$key": "$value"
},
"hostname": "str",
"port": "int",
"username": "str",
"password": "str",
"platform": "str"
}
},
"groups": [
"$group_name"
],
"data": {
"$key": "$value"
},
"hostname": "str",
"port": "int",
"username": "str",
"password": "str",
"platform": "str"
}
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
The `groups_file` follows the same rules as the `hosts_file`. | # groups file
%highlight_file inventory/groups.yaml | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Finally, the defaults file has the same schema as the `Host` we described before but without outer keys to denote individual elements. We will see how the data in the groups and defaults file is used later on in this tutorial. | # defaults file
%highlight_file inventory/defaults.yaml | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Accessing the inventoryYou can access the [inventory](../api/nornir/core/inventory.htmlmodule-nornir.core.inventory) with the `inventory` attribute: | from nornir import InitNornir
nr = InitNornir(config_file="config.yaml")
print(nr.inventory.hosts) | {'host1.cmh': Host: host1.cmh, 'host2.cmh': Host: host2.cmh, 'spine00.cmh': Host: spine00.cmh, 'spine01.cmh': Host: spine01.cmh, 'leaf00.cmh': Host: leaf00.cmh, 'leaf01.cmh': Host: leaf01.cmh, 'host1.bma': Host: host1.bma, 'host2.bma': Host: host2.bma, 'spine00.bma': Host: spine00.bma, 'spine01.bma': Host: spine01.bma, 'leaf00.bma': Host: leaf00.bma, 'leaf01.bma': Host: leaf01.bma}
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
The inventory has two dict-like attributes `hosts` and `groups` that you can use to access the hosts and groups respectively: | nr.inventory.hosts
nr.inventory.groups
nr.inventory.hosts["leaf01.bma"] | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Hosts and groups are also dict-like objects: | host = nr.inventory.hosts["leaf01.bma"]
host.keys()
host["site"] | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Inheritance modelLet's see how the inheritance models works by example. Let's start by looking again at the groups file: | # groups file
%highlight_file inventory/groups.yaml | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
The host `leaf01.bma` belongs to the group `bma` which in turn belongs to the groups `eu` and `global`. The host `spine00.cmh` belongs to the group `cmh` which doesn't belong to any other group.Data resolution works by iterating recursively over all the parent groups and trying to see if that parent group (or any of it's parents) contains the data. For instance: | leaf01_bma = nr.inventory.hosts["leaf01.bma"]
leaf01_bma["domain"] # comes from the group `global`
leaf01_bma["asn"] # comes from group `eu` | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Values in `defaults` will be returned if neither the host nor the parents have a specific value for it. | leaf01_cmh = nr.inventory.hosts["leaf01.cmh"]
leaf01_cmh["domain"] # comes from defaults | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
If nornir can't resolve the data you should get a KeyError as usual: | try:
leaf01_cmh["non_existent"]
except KeyError as e:
print(f"Couldn't find key: {e}") | Couldn't find key: 'non_existent'
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
You can also try to access data without recursive resolution by using the `data` attribute. For example, if we try to access `leaf01_cmh.data["domain"]` we should get an error as the host itself doesn't have that data: | try:
leaf01_cmh.data["domain"]
except KeyError as e:
print(f"Couldn't find key: {e}") | Couldn't find key: 'domain'
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Filtering the inventorySo far we have seen that `nr.inventory.hosts` and `nr.inventory.groups` are dict-like objects that we can use to iterate over all the hosts and groups or to access any particular one directly. Now we are going to see how we can do some fancy filtering that will enable us to operate on groups of hosts based on their properties.The simpler way of filtering hosts is by `` pairs. For instance: | nr.filter(site="cmh").inventory.hosts.keys() | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
You can also filter using multiple `` pairs: | nr.filter(site="cmh", role="spine").inventory.hosts.keys() | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Filter is cumulative: | nr.filter(site="cmh").filter(role="spine").inventory.hosts.keys() | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Or: | cmh = nr.filter(site="cmh")
cmh.filter(role="spine").inventory.hosts.keys()
cmh.filter(role="leaf").inventory.hosts.keys() | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
You can also grab the children of a group: | nr.inventory.children_of_group("eu") | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Advanced filteringSometimes you need more fancy filtering. For those cases you have two options:1. Use a filter function.2. Use a filter object. Filter functionsThe ``filter_func`` parameter let's you run your own code to filter the hosts. The function signature is as simple as ``my_func(host)`` where host is an object of type [Host](../api/nornir/core/inventory.htmlnornir.core.inventory.Host) and it has to return either ``True`` or ``False`` to indicate if you want to host or not. | def has_long_name(host):
return len(host.name) == 11
nr.filter(filter_func=has_long_name).inventory.hosts.keys()
# Or a lambda function
nr.filter(filter_func=lambda h: len(h.name) == 9).inventory.hosts.keys() | _____no_output_____ | Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Filter ObjectYou can also use a filter objects to incrementally create a complex query objects. Let's see how it works by example: | # first you need to import the F object
from nornir.core.filter import F
# hosts in group cmh
cmh = nr.filter(F(groups__contains="cmh"))
print(cmh.inventory.hosts.keys())
# devices running either linux or eos
linux_or_eos = nr.filter(F(platform="linux") | F(platform="eos"))
print(linux_or_eos.inventory.hosts.keys())
# spines in cmh
cmh_and_spine = nr.filter(F(groups__contains="cmh") & F(role="spine"))
print(cmh_and_spine.inventory.hosts.keys())
# cmh devices that are not spines
cmh_and_not_spine = nr.filter(F(groups__contains="cmh") & ~F(role="spine"))
print(cmh_and_not_spine.inventory.hosts.keys()) | dict_keys(['host1.cmh', 'host2.cmh', 'leaf00.cmh', 'leaf01.cmh'])
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
You can also access nested data and even check if dicts/lists/strings contains elements. Again, let's see by example: | nested_string_asd = nr.filter(F(nested_data__a_string__contains="asd"))
print(nested_string_asd.inventory.hosts.keys())
a_dict_element_equals = nr.filter(F(nested_data__a_dict__c=3))
print(a_dict_element_equals.inventory.hosts.keys())
a_list_contains = nr.filter(F(nested_data__a_list__contains=2))
print(a_list_contains.inventory.hosts.keys()) | dict_keys(['host1.cmh', 'host2.cmh'])
| Apache-2.0 | docs/tutorial/inventory.ipynb | brigade-automation/brigade |
Importing Libraries | # important packages
import pandas as pd # data manipulation using dataframes
import numpy as np # data statistical analysis
import seaborn as sns # Statistical data visualization
import cv2 # Image and Video processing library
import matplotlib.pyplot as plt # data visualisation
%matplotlib inline
pd.set_option('display.max_colwidth',1000)
import re # for regular expressions
import nltk # for text manipulation
nltk.download('punkt') # Punkt Sentence Tokenizer
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
from google.colab import drive
drive.mount('/content/drive')
!pwd | /content
| MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Importing Dataset | df = pd.read_csv("/content/drive/My Drive/P1: Twitter Sentiment Analysis/train.txt")
df_test = pd.read_csv("/content/drive/My Drive/P1: Twitter Sentiment Analysis/test_samples.txt")
df.head()
df.shape
df_test.head()
df.info()
df["sentiment"].unique() | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Data Visualization | sns.countplot(df['sentiment'], label = "Count")
positive = df[df["sentiment"] == 'positive']
negative = df[df["sentiment"] == 'negative']
neutral = df[df["sentiment"] == 'neutral']
positive_percentage = (positive.shape[0]/df.shape[0])*100
negative_percentage = (negative.shape[0]/df.shape[0])*100
neutral_percentage = (neutral.shape[0]/df.shape[0])*100
print(f"Positve Tweets = {positive_percentage:.2f}%\nNegative Tweets = {negative_percentage:.2f}%\nNeutral Tweets = {neutral_percentage:.2f}%") | Positve Tweets = 42.23%
Negative Tweets = 15.78%
Neutral Tweets = 41.99%
| MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Data Cleaning | df_test["sentiment"] = "NA"
df_total = pd.concat((df, df_test), ignore_index=True)
df_total.head()
df_total.shape
#### Removing Twitter Handles (@user)
def remove_pattern(input_txt, pattern):
r = re.findall(pattern, input_txt)
for i in r:
input_txt = re.sub(i, '', input_txt)
return input_txt
import re
import nltk
nltk.download('wordnet')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
corpus = []
for i in range(df_total.shape[0]):
text = np.vectorize(remove_pattern)(df_total['tweet_text'][i], "@[\w]*") # Removing Twitter Handles (@user)
text = str(text)
text = re.sub('[^a-zA-Z]', ' ', text) # Removing Punctuations, Numbers, and Special Characters
text = re.sub(r'\s+', ' ', text).strip() # Remove_trailing_spaces(input_txt)
text = text.lower() # Convert to lower case
text = text.split() # Split-data
lemmatizer = WordNetLemmatizer() #WordNet Lemmatization
#ps = PorterStemmer() #porter's stemmer
all_stopwords = stopwords.words('english') # List of Stopwords
all_stopwords.remove('not')
text = [lemmatizer.lemmatize(word) for word in text if not word in set(all_stopwords)]
#text = [ps.stem(word) for word in text if not word in set(all_stopwords)]
text = ' '.join(text)
corpus.append(text)
len(corpus)
### to find no. of sentences with words >500
num = 0
for i in range(len(corpus)):
if (len(corpus[i]) >= 500):
num = num + 1
num | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Data Visualization | sns.countplot(df['sentiment'], label = "Count")
positive = df[df["sentiment"] == 'positive']
negative = df[df["sentiment"] == 'negative']
neutral = df[df["sentiment"] == 'neutral']
positive_percentage = (positive.shape[0]/df.shape[0])*100
negative_percentage = (negative.shape[0]/df.shape[0])*100
neutral_percentage = (neutral.shape[0]/df.shape[0])*100
print(f"Positve Tweets = {positive_percentage:.2f}%\nNegative Tweets = {negative_percentage:.2f}%\nNeutral Tweets = {neutral_percentage:.2f}%") | Positve Tweets = 42.23%
Negative Tweets = 15.78%
Neutral Tweets = 41.99%
| MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Bag of Words Model CountVectorizer | from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
cv = CountVectorizer()
X= cv.fit_transform(corpus).toarray()
len(X[0]) | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
TfidfVectorizer | #from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
#tv = TfidfVectorizer()
#X = tv.fit_transform(corpus).toarray()
len(X[0]) | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Data Splitting | X_train = X[:21465]
X_test = X[21465:]
y_train = df.iloc[:, 1].values
X_train.shape
y_train.shape
from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train) | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Model Prediction | y_pred = classifier.predict(X_test)
print(y_pred)
print(y_pred.shape)
list1 = []
heading = ['tweet_id', 'sentiment']
list1.append(heading)
for i in range(len(y_pred)):
sub = []
sub.append(df_test["tweet_id"][i])
sub.append(y_pred[i])
list1.append(sub) | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Generate Submission File | import csv
with open('/content/drive/My Drive/P1: Twitter Sentiment Analysis/Models/NB_TV.csv', 'w', newline='') as fp:
a = csv.writer(fp, delimiter = ",")
data = list1
a.writerows(data) | _____no_output_____ | MIT | Sentiment_Analysis_of_Twitter_using_Naive_Bayes.ipynb | shreenath2001/Sentiment_Analysis_of_Twitter |
Optical Flow test This notebook will try to follow optical flow initial code comes from : https://docs.opencv.org/3.3.1/d7/d8b/tutorial_py_lucas_kanade.html | %matplotlib inline
import cv2
import os
import sys
import numpy as np
import glob
import matplotlib.pyplot as plt
samplehash = '10a278dc5ebd2b93e1572a136578f9dbe84d10157cc6cca178c339d9ca762c52' #'7fafc640d446cab1872e4376b5c2649f8c67e658b3fc89d2bced3b47c929e608'#
files = sorted(glob.glob( "../data/train/data/" + samplehash + "/frame*.png" ))
images = [ cv2.imread(x,1) for x in files ]
cap = cv2.VideoCapture("vtest.avi")
frame1 = images[0]
prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)
hsv[...,1] = 255
index =0
while(1):
index += 1
if index == 100: break
frame2 = images[index]
next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
hsv[...,0] = ang*180/np.pi/2
hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)
bgr = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
cv2.imshow('frame2',bgr)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
elif k == ord('s'):
cv2.imwrite('opticalfb.png',frame2)
cv2.imwrite('opticalhsv.png',bgr)
prvs = next
cap.release()
cv2.destroyAllWindows() | _____no_output_____ | MIT | analysis/optical flow.ipynb | hitennirmal/goucher |
Comparações ContraintuitivasExistem algumas comparações no Python que não são tão intuitivas quando vemos pela primeira vez, mas que são muito usadas, principalmente por programadores mais experientes.É bom sabermos alguns exemplos e buscar sempre entender o que aquela comparação está buscando verificar. Exemplo 1:Digamos que você está construindo um sistema de controle de vendas e precisa de algumas informações para fazer o cálculo do resultado da loja no fim de um mês. | faturamento = input('Qual foi o faturamento da loja nesse mês?')
custo = input('Qual foi o custo da loja nesse mês?')
if faturamento and custo: # serve para o usuário não deixar os valores vazios
lucro = int(faturamento) - int(custo)
print("O lucro da loja foi de {} reais".format(lucro))
else: # se os valores forem vazios vai aparecer essa mensagem
print('Preencha o faturamento e o lucro corretamente') | Qual foi o faturamento da loja nesse mês?500
Qual foi o custo da loja nesse mês?
Preencha o faturamento e o lucro corretamente
| MIT | if-else/comparacoes-contraintuitivas.ipynb | amarelopiupiu/python |
Conditional Statements in Python Conditional statement controls the flow of execution depending on some condition. Python conditionsPython supports the usual logical conditions from mathematics: | **Condition** | **Expression** | |----:|:----:|| Equal |a == b|| Not Equal |a != b|| Less than |a < b|| Less than or equal to |a <= b|| Greater than |a > b|| Greater than or equal to |a >= b| | a = 2
b = 5
# Equal
a == b
# Not equal
a != b
# Less than
a < b
# Less than or equal to
a <= b
# Greater than
a > b
# Greater than or equal to
a >= b | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
Python Logical Operators:- `and`: Returns True if both statements are true- `or`: Returns True if one of the statements is true- `not`: Reverse the result. Returns False if the result is true, and True is the result is False | a = 1
b = 2
c = 10
# True and True
a < c and b < c
# True and False
a < c and b > c
# True or False
a < c or b > c
# False or True
a > c or b < c
# True or True
a < c or b < c
# False or False
a > c or b > c | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
Using `not` before a boolean expression inverts it: | print(not False)
not(a < c)
not(a > c) | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
If statements | a = 10
b = 20
if b > a:
print("The condition is True")
print('All these sentences are executed!') | The condition is True
All these sentences are executed!
| MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
Remember Python relies on indentation (whitespace at the beginning of a line) to define scope in the code. The same sentence, without indentation, raises an error. | if b > a: # This will raise an error
print("The condition is True")
print('All these sentences are executed') | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
When the condition is False, the sentence is not executed. | a = 10
b = 20
if b < a:
print("The condition is False")
print('These sentences are NOT executed!') | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
The else keyword catches anything which isn't caught by the preceding conditions. | a = 5
b = 10
if b < a:
print("The condition is True.")
else:
print("The condition is False.") | The condition is False.
| MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
The elif keyword is pythons way of saying "if the previous conditions were not true, then try this condition". | # using elif
a = 3
b = 3
if b > a:
print("b is greater than a")
elif a == b:
print("a and b are equal")
# using else
a = 6
b = 4
if b > a:
print("b is greater than a")
elif a == b:
print("a and b are equal")
else:
print("a is greater than b") | a is greater than b
| MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
An arbitrary number of `elif` clauses can be specified. The `else` clause is optional. If it is present, there can be only one, and it must be specified last. | name = 'Anna'
if name == 'Maria':
print('Hello Maria!')
elif name == 'Sarah':
print('Hello Sarah!')
elif name == 'Anna':
print('Hello Anna!')
elif name == 'Sofia':
print('Hello Sofia!')
else:
print("I do not know who you are!")
name = 'Julia'
if name == 'Maria':
print('Hello Maria!')
elif name == 'Sarah':
print('Hello Sarah!')
elif name == 'Anna':
print('Hello Anna!')
elif name == 'Sofia':
print('Hello Sofia!')
else:
print("I do not know who you are!")
# Processing user input
username = input('Enter username:')
print('Your name is', username)
age = input('Enter your age')
if int(age) < 18:
print('You are a child!')
else:
print('You are an adult!')
# Nested if
x = 14
if x > 10:
print('Above 10,')
if x > 20:
print('and also above 20.')
else:
print('but not above 20.')
x = 35
if x > 10:
print('Above 10,')
if x > 20:
print('and also above 20.')
else:
print('but not above 20.') | Above 10,
and also above 20.
| MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
The `pass` Statement: if statements cannot be empty, but if you for some reason have an if statement with no content, put in the `pass` statement to avoid getting an error. | a = 33
b = 200
if b > a:
pass
else:
print('b <= a') | _____no_output_____ | MIT | 02-Basic Python/07-Cond Stat.ipynb | Goliath-Research/Introduction-to-Data-Science |
Numbers In Pi[link](https://www.algoexpert.io/questions/Numbers%20In%20Pi) My Solution | def numbersInPi(pi, numbers):
# Write your code here.
# brute force
d1 = {number: True for number in numbers}
minSpaces = [float('inf')]
numbersInPiHelper(pi, d1, 0, minSpaces, 0)
return minSpaces[0] if minSpaces[0] != float('inf') else -1
def numbersInPiHelper(pi, d1, startIdx, minSpaces, numberOfSpaces):
for endIdx in range(startIdx, len(pi)):
cur = pi[startIdx:endIdx + 1]
if cur in d1:
if endIdx == len(pi) - 1 and numberOfSpaces < minSpaces[0]:
minSpaces[0] = numberOfSpaces
continue
numbersInPiHelper(pi, d1, endIdx + 1, minSpaces, numberOfSpaces + 1)
def numbersInPi(pi, numbers):
# Write your code here.
# dp: O(n^3 + m) time | O(n + m)
# n - the number of digits in pi
# m - length of the number list
d1 = {number: True for number in numbers}
opt = [-1 for i in range(len(pi))]
for i in range(len(pi)):
if pi[:i + 1] in d1:
opt[i] = 0
else:
minValue = float('inf')
for j in range(i):
if opt[j] != -1 and pi[j + 1:i + 1] in d1:
minValue = min(opt[j], minValue)
if minValue != float('inf'):
opt[i] = minValue + 1
return opt[-1] | _____no_output_____ | MIT | algoExpert/numbers_in_pi/solution.ipynb | maple1eaf/learning_algorithm |
Expert Solution | # O(n^3 + m) time | O(n + m) space, where n is the number of digits in Pi and m is the number of favorite numbers
# recursive solution
def numbersInPi(pi, numbers):
numbersTable = {number: True for number in numbers}
minSpaces = getMinSpaces(pi, numbersTable, {}, 0)
return -1 if minSpaces == float("inf") else minSpaces
def getMinSpaces(pi, numbersTable, cache, idx):
if idx == len(pi):
return -1
if idx in cache:
return cache[idx]
minSpaces = float("inf")
for i in range(idx, len(pi)):
prefix = pi[idx: i + 1]
if prefix in numbersTable:
minSpacesInSuffix = getMinSpaces(pi, numbersTable, cache, i + 1)
minSpaces = min(minSpaces, minSpacesInSuffix + 1)
cache[idx] = minSpaces
return cache[idx]
# O(n^3 + m) time | O(n + m) space, where n is the number of digits in Pi and m is the number of favorite numbers
# dp
def numbersInPi(pi, numbers):
numbersTable = {number: True for number in numbers}
cache = {}
for i in reversed(range(len(pi))):
getMinSpaces(pi, numbersTable, cache, i)
return -1 if cache[0] == float("inf") else cache[0]
def getMinSpaces(pi, numbersTable, cache, idx):
if idx == len(pi):
return -1
if idx in cache:
return cache[idx]
minSpaces = float("inf")
for i in range(idx, len(pi)):
prefix = pi[idx : i + 1]
if prefix in numbersTable:
minSpacesInSuffix = getMinSpaces(pi, numbersTable, cache, i + 1)
minSpaces = min(minSpaces, minSpacesInSuffix)
cache[idx] = minSpaces
return cache[idx] | _____no_output_____ | MIT | algoExpert/numbers_in_pi/solution.ipynb | maple1eaf/learning_algorithm |
Introduction to the Harmonic Oscillator *Note:* Much of this is adapted/copied from https://flothesof.github.io/harmonic-oscillator-three-methods-solution.html This week week we are going to begin studying molecular dynamics, which uses classical mechanics to study molecular systems. Our "hydrogen atom" in this section will be the 1D harmomic oscillator.  The harmonic oscillator is a system that, when displaced from its equilibrium position, experiences a restoring force F proportional to the displacement x:$$F=-kx$$The potential energy of this system is $$V = {1 \over 2}k{x^2}$$ These are sometime rewritten as$$ F=- \omega_0^2 m x, \text{ } V(x) = {1 \over 2} \omega_0^2 m {x^2}$$Where $\omega_0 = \sqrt {{k \over m}} $ In classical mechanics, our goal is to determine the equations of motion, $x(t),y(t)$, that describe our system. In this notebook we will use sympy to solve an second order, ordinary differential equation. 1. Solving differential equations with sympy Soliving differential equations can be tough, and there is not always a set plan on how to proceed. Luckily for us, the harmonic osscillator is the classic second order diffferential eqations. Consider the following second order differential equation$$ay(t)''+by(t)'=c$$where $y(t)'' = {{{d^2}y} \over {dt^2}}$, and $y(t)' = {{{d}y} \over {dt}}$ We can rewrite this as a homogeneous linear differential equations$$ay(t)''+by(t)'-c=0$$ The goal here is to find $y(t)$, similar to our classical mechanics problems. Lets use sympy to solve this equation Second order ordinary differential equation First we import the sympy library | import sympy as sym | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Next we initialize pretty printing | sym.init_printing() | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Next we will set our symbols | t,a,b,c=sym.symbols("t,a,b,c") | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Now for somehting new. We can define functions using `sym.Function("f")` | y=sym.Function("y")
y(t) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Now, If I want to define a first or second derivative, I can use `sym.diff` | sym.diff(y(t),(t,1)),sym.diff(y(t),(t,2)) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
My differential equation can be written as follows | dfeq=a*sym.diff(y(t),(t,2))+b*sym.diff(y(t),(t,1))-c
dfeq
sol = sym.dsolve(dfeq)
sol | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
The two constants $C_1$ and $C_2$ can be determined by setting boundry conditions.First, we can set the condition $y(t=0)=y_0$The next intial condition we will set is $y'(t=0)=v_0$To setup the equality we want to solve, we are using `sym.Eq`. This function sets up an equaility between a lhs aand rhs of an equation | # sym.Eq example
alpha,beta=sym.symbols("alpha,beta")
sym.Eq(alpha+2,beta) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Back to the actual problem | y0,v0=sym.symbols("y_0,v_0")
ics=[sym.Eq(sol.args[1].subs(t, 0), y0),
sym.Eq(sol.args[1].diff(t).subs(t, 0), v0)]
ics | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
We can use this result to first solve for $C_2$ and then solve for $C_1$.Or we can use sympy to solve this for us. | solved_ics=sym.solve(ics)
solved_ics | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Substitute the result back into $y(t)$ | full_sol = sol.subs(solved_ics[0])
full_sol | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
We can plot this result too. Assume that $a,b,c=1$ and that the starting conditions are $y_0=0,v_0=0$We will use two sample problems:* case 1 : initial position is nonzero and initial velocity is zero* case 2 : initial position is zero and initialvelocity is nonzero | # Print plots
%matplotlib inline | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Initial velocity set to zero | case1 = sym.simplify(full_sol.subs({y0:0, v0:0, a:1, b:1, c:1}))
case1
sym.plot(case1.rhs)
sym.plot(case1.rhs,(t,-2,2)) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Initial velocity set to one | case2 = sym.simplify(full_sol.subs({y0:0, v0:1, a:1, b:1, c:1}))
case2
sym.plot(case2.lhs,(t,-2,2)) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Calculate the phase space As we will see in lecture, the state of our classical systems are defined as points in phase space, a hyperspace defined by ${{\bf{r}}^N},{{\bf{p}}^N}$. We will convert our sympy expression into a numerical function so that we can plot the path of $y(t)$ in phase space $y,y'$. | case1
# Import numpy library
import numpy as np
# Make numerical functions out of symbolic expressions
yfunc=sym.lambdify(t,case1.rhs,'numpy')
vfunc=sym.lambdify(t,case1.rhs.diff(t),'numpy')
# Make list of numbers
tlst=np.linspace(-2,2,100)
# Import pyplot
import matplotlib
import matplotlib.pyplot as plt
# Make plot
plt.plot(yfunc(tlst),vfunc(tlst))
plt.xlabel('$y$')
plt.ylabel("$y'$")
plt.show() | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
Exercise 1.1 Change the initial starting conditions and see how that changes the plots. Make three different plots with different starting conditions | #Making initial velocity equal 10 and change initial positions to 5,5,5
case3 = sym.simplify(full_sol.subs({y0:1, v0:10, a:5, b:5, c:5}))
case3
sym.plot(case3.rhs)
sym.plot(case3.rhs,(t,-2,2))
case4 = sym.simplify(full_sol.subs({y0:5, v0:2, a:3, b:4, c:5}))
case4
sym.plot(case4.rhs)
sym.plot(case4.rhs,(t,-2,2))
case5 = sym.simplify(full_sol.subs({y0:10, v0:0, a:0, b:1, c:1}))
case5
sym.plot(case5.rhs)
sym.plot(case5.rhs,(t,-2,2)) | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
2. Harmonic oscillator Applying the harmonic oscillator force to Newton's second law leads to the following second order differential equation$$ F = m a $$$$ F= - \omega_0^2 m x $$$$ a = - \omega_0^2 x $$$$ x(t)'' = - \omega_0^2 x $$ The final expression can be rearranged into a second order homogenous differential equation, and can be solved using the methods we used above Your goal is determine and plot the equations of motion of a 1D harmomnic oscillator Exercise 2.1 1. Use the methodology above to determine the equations of motion $x(t), v(t)$ for a harmonic ocillator1. Solve for any constants by using the following initial conditions: $x(0)=x_0, v(0)=v_0$1. Show expressions for and plot the equations of motion for the following cases: 1. $x(0)=0, v(0)=0$ 1. $x(0)=0, v(0)>0$ 1. $x(0)>0, v(0)=0$ 1. $x(0)<0, v(0)=0$1. Plot the phasespace diagram for the harmonic oscillator | # Your code here
m,t,omega=sym.symbols("m,t,omega")
x=sym.Function("x")
x(t)
sym.diff(x(t),(t,1)),sym.diff(x(t),(t,2))
dfeq1=sym.diff(x(t),(t,2))+omega**2*x(t)
dfeq1
sol1 = sym.dsolve(dfeq1)
sol1
x0,v0=sym.symbols("x_0,v_0")
ics1=[sym.Eq(sol1.args[1].subs(t, 0), x0),
sym.Eq(sol1.args[1].diff(t).subs(t, 0), v0)]
ics1
solved_ics1=sym.solve(ics1)
solved_ics1
full_sol1 = sol.subs(solved_ics1[0])
full_sol1
# Print plots
%matplotlib inline
case_100 = sym.simplify(full_sol1.subs({x0:0, v0:0, omega:1}))
case_100
sym.plot(case_100.rhs)
sym.plot(case_100.rhs,(t,-2,2))
case_101 = sym.simplify(full_sol1.subs({x0:0, v0:5, omega:1}))
case_101
sym.plot(case_101.rhs)
sym.plot(case_101.rhs,(t,-2,2))
case_102 = sym.simplify(full_sol1.subs({x0:5, v0:0, omega:1}))
case_102
sym.plot(case_102.rhs)
sym.plot(case_102.rhs,(t,-2,2))
case_103 = sym.simplify(full_sol1.subs({x0:-5, v0:0, omega:1}))
case_103
sym.plot(case_103.rhs)
sym.plot(case_103.rhs,(t,-2,2))
case_100
# Import numpy library
import numpy as np
# Make numerical functions out of symbolic expressions
xfunc=sym.lambdify(t,case_100.rhs,'numpy')
vfunc=sym.lambdify(t,case_100.rhs.diff(t),'numpy')
# Make list of numbers
tlst=np.linspace(-2,2,100)
# Import pyplot
import matplotlib
import matplotlib.pyplot as plt
# Make plot
plt.plot(xfunc(tlst),vfunc(tlst))
plt.xlabel('$y$')
plt.ylabel("$y'$")
plt.show()
# Import numpy library
import numpy as np
# Make numerical functions out of symbolic expressions
xfunc=sym.lambdify(t,case_101.rhs,'numpy')
vfunc=sym.lambdify(t,case_101.rhs.diff(t),'numpy')
# Make list of numbers
tlst=np.linspace(-5,5,100)
# Import pyplot
import matplotlib
import matplotlib.pyplot as plt
# Make plot
plt.plot(xfunc(tlst),vfunc(tlst))
plt.xlabel('$y$')
plt.ylabel("$y'$")
plt.show()
# Import numpy library
import numpy as np
# Make numerical functions out of symbolic expressions
xfunc=sym.lambdify(t,case_102.rhs,'numpy')
vfunc=sym.lambdify(t,case_102.rhs.diff(t),'numpy')
# Make list of numbers
tlst=np.linspace(-5,5,100)
# Import pyplot
import matplotlib
import matplotlib.pyplot as plt
# Make plot
plt.plot(xfunc(tlst),vfunc(tlst))
plt.xlabel('$y$')
plt.ylabel("$y'$")
plt.show() | _____no_output_____ | MIT | harmonic_oscillator.ipynb | sju-chem264-2019/10-24-19-introduction-to-harmonic-oscillator-daliahassan98 |
_Python help: Running the notebook the first time, make sure to run all cells to be able to make changes in the notebook. Hit Shift+Enter to run the cell or click on the top menu: Kernel > Restart & Run All > Restart and Run All Cells to rerun the whole notebook. If you make any changes in a cell, rerun that cell._ Measured data plotting In this notebook you can load radial velocity measurements of multiple galaxies from a prepared Python library. Plot them all in a single graph so you can compare them with each other. Plotting these measurements is the first step of producing a rotation curve for a galaxy and an indication of how much mass the galaxy contains. Setting Newton's law of gravitation and the circular motion equation equal to each other, you can derive the equation for circular velocity in terms of enclosed mass and radius: \begin{equation}v(r) = \sqrt{\frac{G M_{enc}(r)}{r}}\end{equation}>where: $G$ = gravitational constant $M_{enc}(r)$ = enclosed mass as a function of radius $r$ = radius or distance from the center of the galaxy Knowing the radial velocity of stars at different radii, you can estimate the mass that is enclosed in that radius. By measuring the brightnesses (photometric profile) of stars and the amount of gas, you can approximate the mass of "visible" matter. Compare it with the actual mass calculated from the radial velocities to get an idea of how much mass is "missing". The result is a ratio (mass-to-light ratio or M/L) that has been useful to describe the amount of dark matter in galaxies. Vocabulary__Rotation curve__: velocity of stars and gas at distances from the center of the galaxy and plotted as a curve__Radial velocity__: how fast stars and gas are moving at different distances from the center of the galaxy__NGC__: New General Catalogue of galaxies__UGC__: Uppsala General Catalogue of galaxies__kpc__: kiloparsec: 1 kpc = 3262 light years = 3.086e+19 meters = 1.917e+16 miles Load data of multiple galaxies Load the radii, velocities, and errors in velocities of multiple galaxies from a Python library. | # NGC 5533
r_NGC5533, v_NGC5533, v_err_NGC5533 = lg.NGC5533['m_radii'],lg.NGC5533['m_velocities'],lg.NGC5533['m_v_errors']
# NGC 891
r_NGC0891, v_NGC0891, v_err_NGC0891 = lg.NGC0891['m_radii'],lg.NGC0891['m_velocities'],lg.NGC0891['m_v_errors']
# NGC 7814
r_NGC7814, v_NGC7814, v_err_NGC7814 = lg.NGC7814['m_radii'],lg.NGC7814['m_velocities'],lg.NGC7814['m_v_errors']
# NGC 5005
r_NGC5005, v_NGC5005, v_err_NGC5005 = lg.NGC5005['m_radii'],lg.NGC5005['m_velocities'],lg.NGC5005['m_v_errors']
# NGC 3198
r_NGC3198, v_NGC3198, v_err_NGC3198 = lg.NGC3198['m_radii'],lg.NGC3198['m_velocities'],lg.NGC3198['m_v_errors']
# UGC 89
#r_UGC89, v_UGC89, v_err_UGC89 = lg.UGC89['m_radii'],lg.UGC89['m_velocities'],lg.UGC89['m_v_errors']
# UGC 477
r_UGC477, v_UGC477, v_err_UGC477 = lg.UGC477['m_radii'],lg.UGC477['m_velocities'],lg.UGC477['m_v_errors']
# UGC 1281
r_UGC1281, v_UGC1281, v_err_UGC1281 = lg.UGC1281['m_radii'],lg.UGC1281['m_velocities'],lg.UGC1281['m_v_errors']
# UGC 1437
r_UGC1437, v_UGC1437, v_err_UGC1437 = lg.UGC1437['m_radii'],lg.UGC1437['m_velocities'],lg.UGC1437['m_v_errors']
# UGC 2953
r_UGC2953, v_UGC2953, v_err_UGC2953 = lg.UGC2953['m_radii'],lg.UGC2953['m_velocities'],lg.UGC2953['m_v_errors']
# UGC 4325
r_UGC4325, v_UGC4325, v_err_UGC4325 = lg.UGC4325['m_radii'],lg.UGC4325['m_velocities'],lg.UGC4325['m_v_errors']
# UGC 5253
r_UGC5253, v_UGC5253, v_err_UGC5253 = lg.UGC5253['m_radii'],lg.UGC5253['m_velocities'],lg.UGC5253['m_v_errors']
# UGC 6787
r_UGC6787, v_UGC6787, v_err_UGC6787 = lg.UGC6787['m_radii'],lg.UGC6787['m_velocities'],lg.UGC6787['m_v_errors']
# UGC 10075
r_UGC10075, v_UGC10075, v_err_UGC10075 = lg.UGC10075['m_radii'],lg.UGC10075['m_velocities'],lg.UGC10075['m_v_errors'] | _____no_output_____ | MIT | binder/DM_workshop_082721/Interactive_Measured_Data_Plotting.ipynb | villano-lab/galactic-spin |
Plot measured data with errorbars Measured data points of 13 galaxies are plotted below.1. __Change the limits of the x-axis to zoom in and out of the graph.__ _Python help: change the limits of the x-axis by modifying the two numbers (left and right) of the line: plt.xlim then rerun the notebook or the cell._ 2. __Finding supermassive black holes:__ A high velocity at a radius close to zero (close to the center of the galaxy) indicates that there is a supermassive black hole is present at the center of that galaxy, changing the velocities of the close-by stars only. The reason the black hole does not have that much effect on the motion of stars at larger distances is because it acts as a point mass with negligible radius and the velocity drops off as $1 / \sqrt r$. __Can you find the galaxies with a possible central supermassive black hole and hide the curves of the rest of the galaxies?__ _Python help: Turn off the display of all lines and go through them one by one. You can "turn off" the display of each galaxy by typing a sign in front of the line "plt.errorbar".__Sneak peak: In the Interactive_\__Rotation_\__Curve_\__Plotting notebook you will be able to find out which stars are affected by the central supermassive black hole, or in other words, what we mean by "close-by" stars._ 3. __What do you notice about the size of the error bars at radii close to the center and far from the center? What could be the reason?__ | # Define radius for plotting
r = np.linspace(0,100,100)
# Plot
plt.figure(figsize=(10.0,7.0)) # size of the plot
plt.title('Measured data of multiple galaxies', fontsize=14) # giving the plot a title
plt.xlabel('Radius (kpc)', fontsize=12) # labeling the x-axis
plt.ylabel('Velocity (km/s)', fontsize=12) # labeling the y-axis
plt.xlim(0,20) # limits of the x-axis (default from 0 to 20 kpc)
plt.ylim(0,420) # limits of the y-axis (default from 0 to 420 km/s)
# Plotting the measured data
plt.errorbar(r_NGC5533,v_NGC5533,yerr=v_err_NGC5533, label='NGC 5533', marker='o', markersize=6, linestyle='none', color='royalblue')
plt.errorbar(r_NGC0891,v_NGC0891,yerr=v_err_NGC0891, label='NGC 891', marker='o', markersize=6, linestyle='none', color='seagreen')
plt.errorbar(r_NGC7814,v_NGC7814,yerr=v_err_NGC7814, label='NGC 7814', marker='o', markersize=6, linestyle='none', color='m')
plt.errorbar(r_NGC5005,v_NGC5005,yerr=v_err_NGC5005, label='NGC 5005', marker='o', markersize=6, linestyle='none', color='red')
plt.errorbar(r_NGC3198,v_NGC3198,yerr=v_err_NGC3198, label='NGC 3198', marker='o', markersize=6, linestyle='none', color='gold')
plt.errorbar(r_UGC477,v_UGC477,yerr=v_err_UGC477, label='UGC 477', marker='o', markersize=6, linestyle='none', color='lightpink')
plt.errorbar(r_UGC1281,v_UGC1281,yerr=v_err_UGC1281, label='UGC 1281', marker='o', markersize=6, linestyle='none', color='aquamarine')
plt.errorbar(r_UGC1437,v_UGC1437,yerr=v_err_UGC1437, label='UGC 1437', marker='o', markersize=6, linestyle='none', color='peru')
plt.errorbar(r_UGC2953,v_UGC2953,yerr=v_err_UGC2953, label='UGC 2953', marker='o', markersize=6, linestyle='none', color='lightslategrey')
plt.errorbar(r_UGC4325,v_UGC4325,yerr=v_err_UGC4325, label='UGC 4325', marker='o', markersize=6, linestyle='none', color='darkorange')
plt.errorbar(r_UGC5253,v_UGC5253,yerr=v_err_UGC5253, label='UGC 5253', marker='o', markersize=6, linestyle='none', color='maroon')
plt.errorbar(r_UGC6787,v_UGC6787,yerr=v_err_UGC6787, label='UGC 6787', marker='o', markersize=6, linestyle='none', color='midnightblue')
plt.errorbar(r_UGC10075,v_UGC10075,yerr=v_err_UGC10075, label='UGC 10075', marker='o', markersize=6, linestyle='none', color='y')
plt.legend(loc='upper right')
plt.show()
# Time
executionTime = (time.time() - startTime)
ttt=executionTime/60
print(f'Execution time: {ttt:.2f} minutes') | Execution time: 8.77 minutes
| MIT | binder/DM_workshop_082721/Interactive_Measured_Data_Plotting.ipynb | villano-lab/galactic-spin |
Import packages | #
import sklearn
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
define two functions for visualization | #
from matplotlib.colors import ListedColormap
def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):
x1s = np.linspace(axes[0], axes[1], 100)
x2s = np.linspace(axes[2], axes[3], 100)
x1, x2 = np.meshgrid(x1s, x2s)
X_new = np.c_[x1.ravel(), x2.ravel()]
y_pred = clf.predict(X_new).reshape(x1.shape)
custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
if not iris:
custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
if plot_training:
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", label="Iris-Setosa")
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", label="Iris-Versicolor")
plt.plot(X[:, 0][y==2], X[:, 1][y==2], "g^", label="Iris-Virginica")
plt.axis(axes)
if iris:
plt.xlabel("Petal length", fontsize=14)
plt.ylabel("Petal width", fontsize=14)
else:
plt.xlabel(r"$x_1$", fontsize=18)
plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
if legend:
plt.legend(loc="lower right", fontsize=14)
#
def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel="$y$"):
x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)
y_pred = tree_reg.predict(x1)
plt.axis(axes)
plt.xlabel("$x_1$", fontsize=18)
if ylabel:
plt.ylabel(ylabel, fontsize=18, rotation=0)
plt.plot(X, y, "b.")
plt.plot(x1, y_pred, "r.-", linewidth=2, label=r"$\hat{y}$") | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Load the Iris data from sklearn. Use petal length and width as the training input. | from sklearn.datasets import load_iris
iris = load_iris()
iris.feature_names
X = iris.data[:, 2:]
y = iris.target | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Now fit a decision tree classifier. Set max depth at 2. | from sklearn.tree import DecisionTreeClassifier
clf_tree = DecisionTreeClassifier(max_depth=2)
clf_tree.fit(X, y) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Now visualize the model by the plot_decision_boundary function. | plt.figure(figsize=(10, 6))
plot_decision_boundary(clf_tree, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Predict the probability of each class for [5, 1.5] | clf_tree.predict_proba([[5, 1.5]]) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Run next cell to generate 100 moon data at noise=0.25 and random_state=53 | from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.25, random_state=53) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Now fit two decision tree model. One has no restriction, and another has min_samples_leaf = 4 | clf_tree = DecisionTreeClassifier()
clf_tree_4 = DecisionTreeClassifier(min_samples_leaf=4)
clf_tree.fit(X, y)
clf_tree_4.fit(X, y) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Now use function plot_decision_boundary to visualize and compare these two models. Check for overfitting. | limit = [X[:, 0].min(), X[:, 0].max(), X[:, 1].min(), X[:, 1].max()]
plt.figure(figsize=(12, 6))
plt.subplot(121)
plot_decision_boundary(clf_tree, X, y, axes=limit, iris=False)
plt.title('no restriction')
plt.subplot(122)
plot_decision_boundary(clf_tree_4, X, y, axes=limit, iris=False)
plt.title('min_samples_leaf=4') | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Regression Run next cell to generate synthetic data | #
np.random.seed(42)
m = 200
X = np.random.rand(m, 1)
y = 4 * (X - 0.5) ** 2
y = y + np.random.randn(m, 1) / 10
y = y.ravel() | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Fit two regression trees. The first three have max_depth of 2, 3, 5; and the last one has no restriction. | from sklearn.tree import DecisionTreeRegressor
reg_tree_2 = DecisionTreeRegressor(max_depth=2)
reg_tree_3 = DecisionTreeRegressor(max_depth=3)
reg_tree_5 = DecisionTreeRegressor(max_depth=5)
reg_tree_none = DecisionTreeRegressor()
reg_tree_2.fit(X, y)
reg_tree_3.fit(X, y)
reg_tree_5.fit(X, y)
reg_tree_none.fit(X, y) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Now visualize these four trees by the function of plot_regression_predictions | limit = [X.min(), X.max(), y.min(), y.max()]
plt.figure(figsize=(10, 10))
plt.subplot(221)
plot_regression_predictions(reg_tree_2, X, y, axes=limit)
plt.subplot(222)
plot_regression_predictions(reg_tree_3, X, y, axes=limit)
plt.subplot(223)
plot_regression_predictions(reg_tree_5, X, y, axes=limit)
plt.subplot(224)
plot_regression_predictions(reg_tree_none, X, y, axes=limit) | _____no_output_____ | MIT | Chapter6.ipynb | VandyChris/HandsOnMachineLearning |
Read the actual Bird Data CSV Convert Cornell Bird Migration CSV files to CZMLTrying out the CZML python package. Installed from PIP until we can build our own conda package | import pandas as pd
import datetime as dt
import numpy as np
# parser to convert integer yeardays to datetimes in 2015
def parse(day):
date = dt.datetime(2015,1,1,0,0) + dt.timedelta(days=(day.astype(np.int32)-1))
return date
def csv_to_position(file='Acadian_Flycatcher.csv'):
df = pd.read_csv(file, parse_dates=True, date_parser=parse, index_col=0, na_values='NA')
df.dropna(how="all", inplace=True)
df['z']=0.0
df['str']= df.index.strftime('%Y-%m-%d')
df2 = df.ix[:,[3,0,1,2]]
a = df2.values.tolist()
return {'cartographicDegrees':[val for sublist in a for val in sublist]}
import glob
csv_files = glob.glob('*.csv')
for csv_file in csv_files:
bird = csv_file.split('.')[0]
packet = czml.CZMLPacket(id=bird, availability="2015-01-01/2015-12-31")
packet.point = point
pos = csv_to_position(file=csv_file)
packet.position = pos
desc = czml.Description(string=bird)
packet.description = desc
doc.packets.append(packet)
# inspect the last packet
packet.dumps()
# Write the CZML document to a file
filename = "all_birds.czml"
doc.write(filename) | _____no_output_____ | CC0-1.0 | birds/bird_csv_to_czml.ipynb | rsignell-usgs/CZML |
CoreNLP running on Python Server | !echo "Downloading CoreNLP..."
!wget "http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip" -O corenlp.zip
!unzip corenlp.zip
!mv ./stanford-corenlp-full-2018-10-05 ./corenlp
# Set the CORENLP_HOME environment variable to point to the installation location
import os
os.environ["CORENLP_HOME"] = "./corenlp"
# Import client module
from stanfordnlp.server import CoreNLPClient
# Construct a CoreNLPClient with some basic annotators, a memory allocation of 4GB, and port number 9001
client = CoreNLPClient(annotators=['tokenize','ssplit', 'pos', 'lemma', 'ner'], memory='4G', endpoint='http://localhost:9001')
print(client)
# Start the background server and wait for some time
# Note that in practice this is totally optional, as by default the server will be started when the first annotation is performed
client.start()
import time; time.sleep(10) | <stanfordnlp.server.client.CoreNLPClient object at 0x7f6ed0cf68d0>
Starting server with command: java -Xmx4G -cp ./corenlp/* edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9001 -timeout 60000 -threads 5 -maxCharLength 100000 -quiet True -serverProperties corenlp_server-5f4e4d7044944e52.props -preload tokenize,ssplit,pos,lemma,ner
| MIT | Colab NoteBooks/BertSum_and_PreSumm_preprocessing.ipynb | gagan94/PreSumm |
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