markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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下面这个的这个拦截特征比较明显，两天前才发生向上跳空的交易： | print('失败概率最大的分类簇{0}'.format(ump_jump.cprs.lrs.argmax()))
# 拿出跳空失败概率最大的分类簇
max_failed_cluster_orders = ump_jump.nts[ump_jump.cprs.lrs.argmax()]
# 显示失败概率最大的分类簇，表11-6所示
max_failed_cluster_orders
ml.show_orders_hist(max_failed_cluster_orders, feature_columns=['buy_diff_up_days', 'buy_jump_up_power',
... | ipython/第十一章-量化系统——机器学习•ABU.ipynb | bbfamily/abu | gpl-3.0 |
11.2.4 价格主裁
请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容 | from abupy import AbuUmpMainPrice
ump_price = AbuUmpMainPrice.ump_main_clf_dump(orders_pd_train, save_order=False)
ump_price.fiter.df.head()
print('失败概率最大的分类簇{0}'.format(ump_price.cprs.lrs.argmax()))
# 拿出价格失败概率最大的分类簇
max_failed_cluster_orders = ump_price.nts[ump_price.cprs.lrs.argmax()]
# 表11-8所示
max_failed_cluster_... | ipython/第十一章-量化系统——机器学习•ABU.ipynb | bbfamily/abu | gpl-3.0 |
11.2.5 波动主裁
请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容 | from abupy import AbuUmpMainWave
ump_wave = AbuUmpMainWave.ump_main_clf_dump(orders_pd_train, save_order=False)
ump_wave.fiter.df.head()
print('失败概率最大的分类簇{0}'.format(ump_wave.cprs.lrs.argmax()))
# 拿出波动特征失败概率最大的分类簇
max_failed_cluster_orders = ump_wave.nts[ump_wave.cprs.lrs.argmax()]
# 表11-10所示
max_failed_cluster_order... | ipython/第十一章-量化系统——机器学习•ABU.ipynb | bbfamily/abu | gpl-3.0 |
11.2.6 验证主裁是否称职
请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容 | # 选取有交易结果的数据order_has_result
order_has_result = abu_result_tuple_test.orders_pd[abu_result_tuple_test.orders_pd.result != 0]
ump_wave.best_hit_cnt_info(ump_wave.llps)
from abupy import AbuUmpMainDeg, AbuUmpMainJump, AbuUmpMainPrice, AbuUmpMainWave
ump_deg = AbuUmpMainDeg(predict=True)
ump_jump = AbuUmpMainJump(predic... | ipython/第十一章-量化系统——机器学习•ABU.ipynb | bbfamily/abu | gpl-3.0 |
<hr> Turning on tooltips
Many plot types let you specify tooltips with the labels argument and the tooltips=True setting. First, turn on the setting for a simple scatter plot, and try clicking a point -- you should see it's x and y value appear above. | x = random.rand(10)
y = random.rand(10)
lgn.scatter(x, y, size=10, tooltips=True) | misc/tooltips.ipynb | RaoUmer/lightning-example-notebooks | mit |
Now let's try adding explicit text labels. We'll make labels maked on random group assignments. | x = random.rand(10)
y = random.rand(10)
g = (random.rand(10) * 5).astype('int')
lgn.scatter(x, y, size=10, labels=['group ' + str(i) for i in g], tooltips=True, group=g) | misc/tooltips.ipynb | RaoUmer/lightning-example-notebooks | mit |
<hr> Labeling graph vertices
A common use case for tooltips is in labeling graphs. Here we'll make a simple force network and label the vertices based on a group assignment. | mat = random.rand(25,25)
mat[mat<0.8] = 0
group = (random.rand(25) * 5).astype('int')
labels = ['vertex ' + str(g) for g in group]
lgn.force(mat, labels=labels, group=group) | misc/tooltips.ipynb | RaoUmer/lightning-example-notebooks | mit |
2) What's the current wind speed? How much warmer does it feel than it actually is? | print('The current wind speed is', data['currently']['windSpeed'], 'miles per hour.')
print('It feels', round(data['currently']['apparentTemperature'] - data['currently']['temperature'], 2), 'degrees Fahrenheit warmer than it actually is.') | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
3) The first daily forecast is the forecast for today. For the place you decided on up above, how much of the moon is currently visible? | # #temp. Answer: dict
# print(type(data['daily']))
# #temp. Answer: ['summary', 'data', 'icon']
# print(data['daily'].keys())
# #temp. Answer: list
# print(type(data['daily']['data']))
# #temp. It's a list of dictionaries
# #this time means Wed, 08 Jun 2016 05:00:00 GMT, which is currently today
# print(data['daily']['... | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
4) What's the difference between the high and low temperatures for today? | print('The difference between today\'s high and low temperatures is', round(data['daily']['data'][0]['temperatureMax'] - data['daily']['data'][0]['temperatureMin'], 2), 'degrees Fahrenheit.') | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
5) Loop through the daily forecast, printing out the next week's worth of predictions. I'd like to know the high temperature for each day, and whether it's hot, warm, or cold, based on what temperatures you think are hot, warm or cold. | daily_forecast = data['daily']['data']
print('Starting with today\'s, the forecasts for the next week are for highs of:')
for day in daily_forecast:
if 85 <= day['temperatureMax']:
warmth = 'hot'
elif 70 <= day['temperatureMax'] < 85:
warmth = 'warm'
else:
warmth = 'cold'
print(... | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
6) What's the weather looking like for the rest of today in Miami, Florida? I'd like to know the temperature for every hour, and if it's going to have cloud cover of more than 0.5 say "{temperature} and cloudy" instead of just the temperature. | fl_url = 'https://api.forecast.io/forecast/' + apikey + '/' + coordinates['Miami']
fl_response = requests.get(url)
fl_data = fl_response.json()
# #temp. Answer: dict
# print(type(fl_data['hourly']))
# #temp. Answer: ['summary', 'data', 'icon']
# print(fl_data['hourly'].keys())
# #temp. Answer: list
# print(type(fl_dat... | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
7) What was the temperature in Central Park on Christmas Day, 1980? How about 1990? 2000?
Tip: You'll need to use UNIX time, which is the number of seconds since January 1, 1970. Google can help you convert a normal date!
Tip: You'll want to use Forecast.io's "time machine" API at https://developer.forecast.io/docs/v2 | decades = range(3)
for decade in decades:
cp_url = 'https://api.forecast.io/forecast/' + apikey + '/' + coordinates['Central Park'] + ',' + str(10 * decade + 1980) + '-12-25T12:00:00'
cp_response = requests.get(cp_url)
cp_data = cp_response.json()
print('On Christmas Day in', str(1980 + decade * 10) + ... | 06/homework-6-schuetz_graded.ipynb | raschuetz/foundations-homework | mit |
Slicing our Corpus
To generate a time-variant network, we must first slice our Corpus temporally. Many research questions about social networks like coauthor networks involve how nodes recruit new neighbors. To look at this in the context of our dataset, we'll want to keep old nodes and edges around even if they don't ... | MyCorpus.slice(window_size=3, cumulative=True) | .ipynb_checkpoints/4. Time-variant networks-checkpoint.ipynb | diging/tethne-notebooks | gpl-3.0 |
The following code builds a collection of co-authorship networks, using a 3-year cumulative time-window. | MyGraphCollection = GraphCollection(MyCorpus, coauthors, slice_kwargs={'window_size': 5, 'step_size': 2}) | .ipynb_checkpoints/4. Time-variant networks-checkpoint.ipynb | diging/tethne-notebooks | gpl-3.0 |
Analyzing time-variant networks
The GraphCollection makes it easy to apply algorithms from NetworkX across the whole time-variant network (i.e. to all graphs in the GraphCollection).
The method analyze applies an algorithm to all of the graphs in the GraphCollection. | dc = MyGraphCollection.analyze('degree_centrality')
dc[1986].items()[20:30]
bcentrality = MyGraphCollection.analyze('betweenness_centrality') | .ipynb_checkpoints/4. Time-variant networks-checkpoint.ipynb | diging/tethne-notebooks | gpl-3.0 |
Some algorithms, like "degree_centrality" and "betweenness_centrality" return a value for each node in each graph. In that case, the nodes in each graph are updated with those values. | MyGraphCollection[2008].nodes(data=True)[15:17] # Shows the attributes for two of the nodes in the 2008 graph. | .ipynb_checkpoints/4. Time-variant networks-checkpoint.ipynb | diging/tethne-notebooks | gpl-3.0 |
The method plot_attr_distribution can help to visualize the results of an algorithm across the graphs in the GraphCollection. In the example below, attr='degree_centrality' selects the degree_centrality attribute, etype='node' indicates that the attribute belongs to nodes (not edges), and stat=mean specifies that the P... | node_id = MyGraphCollection.node_lookup[(u'WARWICK', u'SI')]
warwick_centrality = MyGraphCollection.node_history(node_id, 'degree_centrality')
warwick_centrality.items()[:20] # First 20 years.
plt.plot(warwick_centrality.keys(), warwick_centrality.values(), 'ro')
plt.ylabel('Degree Centrality')
plt.show() | .ipynb_checkpoints/4. Time-variant networks-checkpoint.ipynb | diging/tethne-notebooks | gpl-3.0 |
Algebraic Equations
Write a function that computes the quadratic equation. | def quadratic():
return ???
quadratic() | tutorial_exercises/Advanced-Solvers.ipynb | leosartaj/scipy-2016-tutorial | bsd-3-clause |
Write a function that computes the general solution to the cubic $x^3 + ax^2 + bx + c$. | def cubic():
return ???
cubic() | tutorial_exercises/Advanced-Solvers.ipynb | leosartaj/scipy-2016-tutorial | bsd-3-clause |
The API needs a file APIKEY with your API key in the work folder. We initialize a datahub and dataset objects. | dh = datahub.datahub(server='api.planetos.com',version='v1')
ds = dataset.dataset('ncep_cfsv2', dh, debug=False)
ds.vars=variables.variables(ds.variables(), {'reftimes':ds.reftimes,'timesteps':ds.timesteps},ds) | api-examples/CFSv2_usage_example.ipynb | planet-os/notebooks | mit |
In order to the automatic location selection to work, add your custom location to the API_client.python.lib.predef_locations file. | for locat in ['Võru']:
ds.vars.Convective_Precipitation_Rate_surface.get_values(count=1000, location=locat, reftime='2018-04-20T18:00:00',
reftime_end='2018-05-02T18:00:00')
ds.vars.Maximum_temperature_height_above_ground.get_values(count=1000, locatio... | api-examples/CFSv2_usage_example.ipynb | planet-os/notebooks | mit |
Here we clean the table just a bit and create time based index. | ddd = ds.vars.Convective_Precipitation_Rate_surface.values['Võru'][['reftime','time','Convective_Precipitation_Rate_surface']]
dd_test=ddd.set_index('time') | api-examples/CFSv2_usage_example.ipynb | planet-os/notebooks | mit |
Next, we resample the data to 1-month totals. | reft_unique = ds.vars.Convective_Precipitation_Rate_surface.values['Võru']['reftime'].unique()
nf = []
for reft in reft_unique:
abc = dd_test[dd_test.reftime==reft].resample('M').sum()
abc['Convective_Precipitation_Rate_surface'+'_'+reft.astype(str)] = \
abc['Convective_Precipitation_Rate_surface']*6*3... | api-examples/CFSv2_usage_example.ipynb | planet-os/notebooks | mit |
Finally, we are visualizing the monthly precipitation for each different forecast, in a single plot. | fig=plt.figure(figsize=(10,8))
nf2.transpose().boxplot()
plt.ylabel('Monthly precipitation mm')
fig.autofmt_xdate()
plt.show() | api-examples/CFSv2_usage_example.ipynb | planet-os/notebooks | mit |
Si se fijan, no hay ningún close en esa porción de código, pero sin embargo al salir del bloque que encierra el with, el archivo se encuentra cerrado sin importar si salió exitosamente o con una excepción
Pickles
Los pickles son una forma de guardar estructuras de datos complejas y recuperarlas fácilmente, sin necesida... | import pickle # Importo la biblioteca necesaria
# Creo la variable archivo
with open('ejemplo.pkl', 'wb') as archivo:
pkl = pickle.Pickler(archivo) # Creo mi punto de acceso a los datos a partir del archivo
lista1 = [1, 2, 3]
lista2 = [4, 5]
diccionario = {'campo1': 1, 'campo2': 'dos'}
pkl.dump... | .ipynb_checkpoints/Clase 06 - Archivos binarios, Apareo de archivos-checkpoint.ipynb | gsorianob/fiuba-python | apache-2.0 |
Para leer de un archivo pickle no puedo usar el método readline que usa la estructura for, por lo que no me queda otra que siempre intentar leer y cuando lance una excepción del tipo EOFError dejar de hacerlo. | with open('ejemplo.pkl', 'rb') as archivo:
seguir_leyendo = True
while seguir_leyendo:
try:
data = pickle.load(archivo) # Leo del archivo un elemento
except EOFError:
seguir_leyendo = False
else:
print '### Esta línea no es del archivo ###'
... | .ipynb_checkpoints/Clase 06 - Archivos binarios, Apareo de archivos-checkpoint.ipynb | gsorianob/fiuba-python | apache-2.0 |
Ejemplo 2: Guardo una lista de elementos
Así como guardo de a un elemento por vez, también puedo guardar una lista que tenga todos los elementos que tenga en memoria: | lista = [ # Creo la lista que quiero guardar
{'usuario': 'usuario1', 'puntaje': 5},
{'usuario': 'usuario2', 'puntaje': 3},
{'usuario': 'usuario3', 'puntaje': 1},
]
# Guardo la lista en el archivo
with open('ejemplo_2.pkl', 'wb') as archivo:
pkl = pickle.Pickler(archivo)
pkl.dump(lista)
# Leo d... | .ipynb_checkpoints/Clase 06 - Archivos binarios, Apareo de archivos-checkpoint.ipynb | gsorianob/fiuba-python | apache-2.0 |
First, notice that the FullAdder is a subclass of Circuit. All Magma circuits are classes in python.
Second, the attribute IO defines the interface to the circuit.
IO is a list of alternating keys and values.
The key is the name of the argument, and the value is the type.
In this circuit, all the inputs and outputs ... | from magma.simulator import PythonSimulator
fulladder_magma = PythonSimulator(FullAdder)
assert fulladder_magma(1, 0, 0) == fulladder(1, 0, 0), "Failed"
assert fulladder_magma(0, 1, 0) == fulladder(0, 1, 0), "Failed"
assert fulladder_magma(1, 1, 0) == fulladder(1, 1, 0), "Failed"
assert fulladder_magma(1, 0, 1) == fu... | notebooks/tutorial/coreir/coreir-tutorial/full_adder.ipynb | phanrahan/magmathon | mit |
Here is another way to test the circuit.
We define a set of test vectors and plot them in python. | from magma.waveform import waveform
test_vectors_raw = [
[0, 0, 0, 0, 0],
[0, 0, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 0, 1],
[1, 0, 0, 1, 0],
[1, 0, 1, 0, 1],
[1, 1, 0, 0, 1],
[1, 1, 1, 1, 1]
]
waveform(test_vectors_raw, ["a", "b", "cin", "sum", "cout"]) | notebooks/tutorial/coreir/coreir-tutorial/full_adder.ipynb | phanrahan/magmathon | mit |
We can use the simulator to also generate a set of test vectors. | from fault.test_vectors import generate_simulator_test_vectors
from bit_vector import BitVector
test_vectors = [
[BitVector(x) for x in test_vector]
for test_vector in test_vectors_raw
]
tests = generate_simulator_test_vectors(FullAdder, flatten=False)
| notebooks/tutorial/coreir/coreir-tutorial/full_adder.ipynb | phanrahan/magmathon | mit |
Finally, compare the simulated test vectors to the expected values. | print( "Success" if tests == test_vectors else "Failure" ) | notebooks/tutorial/coreir/coreir-tutorial/full_adder.ipynb | phanrahan/magmathon | mit |
The last step we will do is generate coreir and verilog for the full adder circuit. | m.compile("build/FullAdder", FullAdder, output="coreir")
%cat build/FullAdder.json
m.compile("build/FullAdder", FullAdder, output="coreir-verilog")
%cat build/FullAdder.v | notebooks/tutorial/coreir/coreir-tutorial/full_adder.ipynb | phanrahan/magmathon | mit |
Fast Fourier Transform snippets
Documentation
Numpy implementation: http://docs.scipy.org/doc/numpy/reference/routines.fft.html
Scipy implementation: http://docs.scipy.org/doc/scipy/reference/fftpack.html
Import directives | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm | python_numpy_fourier_transform_en.ipynb | jdhp-docs/python-notebooks | mit |
Make data | pattern = np.zeros((4, 4))
pattern[1:3,1:3] = 1
pattern
signal = np.tile(pattern, (2, 2))
fig = plt.figure(figsize=(16.0, 10.0))
ax = fig.add_subplot(111)
ax.imshow(signal, interpolation='nearest', cmap=cm.gray) | python_numpy_fourier_transform_en.ipynb | jdhp-docs/python-notebooks | mit |
Fourier transform with Numpy
Do the fourier transform | transformed_signal = np.fft.fft2(signal)
#transformed_signal
fig = plt.figure(figsize=(16.0, 10.0))
ax = fig.add_subplot(111)
ax.imshow(abs(transformed_signal), interpolation='nearest', cmap=cm.gray) | python_numpy_fourier_transform_en.ipynb | jdhp-docs/python-notebooks | mit |
Filter | max_value = np.max(abs(transformed_signal))
filtered_transformed_signal = transformed_signal * (abs(transformed_signal) > max_value*0.5)
#filtered_transformed_signal[6, 6] = 0
#filtered_transformed_signal[2, 2] = 0
#filtered_transformed_signal[2, 6] = 0
#filtered_transformed_signal[6, 2] = 0
#filtered_transformed_sign... | python_numpy_fourier_transform_en.ipynb | jdhp-docs/python-notebooks | mit |
Do the reverse transform | filtered_signal = np.fft.ifft2(filtered_transformed_signal)
#filtered_signal
fig = plt.figure(figsize=(16.0, 10.0))
ax = fig.add_subplot(111)
ax.imshow(abs(filtered_signal), interpolation='nearest', cmap=cm.gray)
#shifted_filtered_signal = np.fft.ifftshift(transformed_signal)
#shifted_filtered_signal
#shifted_transf... | python_numpy_fourier_transform_en.ipynb | jdhp-docs/python-notebooks | mit |
Isotherm display
To generate a quick plot of an isotherm, call the plot() function. The parameters to this function are the same as pygaps.plot_iso. | isotherm = next(i for i in isotherms_n2_77k if i.material=='MCM-41')
ax = isotherm.plot() | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
Isotherm plotting and comparison
For more complex plots of multiple isotherms, the pygaps.plot_iso function is provided. Several examples of isotherm plotting are presented here:
A logarithmic isotherm graph comparing the adsorption branch of two isotherms up to 1 bar (x_range=(None, 1)).
The isotherms are measured ... | import pygaps.graphing as pgg
ax = pgg.plot_iso(
isotherms_isosteric,
branch = 'ads',
logx = True,
x_range=(None,1),
lgd_keys=['temperature'],
loading_unit='cm3(STP)',
color=['b', 'r', 'g']
) | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
A black and white (color=False) full scale graph of both adsorption and desorption branches of an
isotherm (branch = 'all'), saving it to the local directory for a publication (save_path=path). The result file is found here. We also display the isotherm points using X markers (marker=['x']) and set the figure title (... | import pygaps.graphing as pgg
from pathlib import Path
path = Path.cwd() / 'novel.png'
isotherm = next(i for i in isotherms_n2_77k if i.material=='MCM-41')
ax = pgg.plot_iso(
isotherm,
branch = 'all',
color=False,
save_path=path,
marker=['x'],
) | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
A graph which plots the both the loading and enthalpy as a function of pressure on the left
and the enthalpy as a function of loading on the right, for a microcalorimetry experiment.
To do this, we separately generate the axes and pass them in to the plot_iso function (ax=ax1).
We want the legend to appear inside... | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))
pgg.plot_iso(
isotherms_calorimetry[1],
ax=ax1,
x_data='pressure',
y1_data='loading',
y2_data='enthalpy',
lgd_pos='lower right',
y2_range=(0,40),
y1_line_style=dict(markersize=0),
y2_line_style=dict(markersize=3),
)
pgg.plot_iso(
... | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
A comparison graph of all the nitrogen isotherms, with both branches shown but without adding the desorption branch to the label (branch='all-nol'). We want each isotherm to use a different marker (marker=len(isotherms)) and to not display the desorption branch component of the legend (only lgd_keys=['material']). | ax = pgg.plot_iso(
isotherms_n2_77k,
branch='all',
lgd_keys=['material'],
marker=len(isotherms_n2_77k)
)
ax.set_title("Regular isotherms colour") | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
A black and white version of the same graph (color=False), but with absolute pressure in bar. | ax = pgg.plot_iso(
isotherms_n2_77k,
branch='all',
color=False,
lgd_keys=['material'],
pressure_mode='absolute',
pressure_unit='bar',
)
ax.set_title("Black and white") | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
Only some ranges selected for display from all the isotherms (x_range=(0.2, 0.6) and y1_range=(3, 10)). | ax = pgg.plot_iso(
isotherms_n2_77k,
branch='all',
x_range=(0.2, 0.6),
y1_range=(3, 10),
lgd_keys=['material']
) | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
The isosteric pressure isotherms, in relative pressure mode and loading in cm3(STP). No markers
are displayed (marker=False). | ax = pgg.plot_iso(
isotherms_isosteric,
branch='ads',
pressure_mode='relative',
loading_unit='cm3(STP)',
lgd_keys=['adsorbate', 'temperature'],
marker=False
)
ax.set_title("Different pressure mode or units") | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
Only desorption branch of some isotherms (branch='des'), displaying the user who recorded the isotherms
in the graph legend. | ax = pgg.plot_iso(
isotherms_n2_77k,
branch='des',
lgd_keys=['material', 'user'],
lgd_pos='out bottom',
)
ax.set_title("Only desorption branch") | docs/examples/plotting.ipynb | pauliacomi/pyGAPS | mit |
plot_images() is used to plot several images in the same figure. It supports many configurations and has many options available to customize the resulting output. The function returns a list of matplotlib axes, which can be used to further customize the figure. Some examples are given below.
Default usage
A common usag... | import scipy.ndimage
image = hs.signals.Signal2D(np.random.random((2, 3, 512, 512)))
for i in range(2):
for j in range(3):
image.data[i,j,:] = scipy.misc.ascent()*(i+0.5+j)
axes = image.axes_manager
axes[2].name = "x"
axes[3].name = "y"
axes[2].units = "nm"
axes[3].units = "nm"
image.metadata.... | hyperspy/tests/drawing/test_plot_image.ipynb | vidartf/hyperspy | gpl-3.0 |
Specified labels
By default, plot_images() will attempt to auto-label the images based on the Signal titles. The labels (and title) can be customized with the label and suptitle arguments. In this example, the axes labels and ticks are also disabled with axes_decor: | import scipy.ndimage
image = hs.signals.Signal2D(np.random.random((2, 3, 512, 512)))
for i in range(2):
for j in range(3):
image.data[i,j,:] = scipy.misc.ascent()*(i+0.5+j)
axes = image.axes_manager
axes[2].name = "x"
axes[3].name = "y"
axes[2].units = "nm"
axes[3].units = "nm"
image.metadata.... | hyperspy/tests/drawing/test_plot_image.ipynb | vidartf/hyperspy | gpl-3.0 |
List of images
plot_images() can also be used to easily plot a list of Images, comparing different Signals, including RGB images. This example also demonstrates how to wrap labels using labelwrap (for preventing overlap) and using a single colorbar for all the Images, as opposed to multiple individual ones: | import scipy.ndimage
# load red channel of raccoon as an image
image0 = hs.signals.Signal2D(scipy.misc.face()[:,:,0])
image0.metadata.General.title = 'Rocky Raccoon - R'
axes0 = image0.axes_manager
axes0[0].name = "x"
axes0[1].name = "y"
axes0[0].units = "mm"
axes0[1].units = "mm"
# load lena into 2x3 hyperimage
imag... | hyperspy/tests/drawing/test_plot_image.ipynb | vidartf/hyperspy | gpl-3.0 |
Real-world use
Another example for this function is plotting EDS line intensities. Using a spectrum image with EDS data, one can use the following commands to get a representative figure of the line intensities. This example also demonstrates changing the colormap (with cmap), adding scalebars to the plots (with scaleb... | from urllib.request import urlretrieve
url = 'http://cook.msm.cam.ac.uk//~hyperspy//EDS_tutorial//'
urlretrieve(url + 'core_shell.hdf5', 'core_shell.hdf5')
si_EDS = hs.load("core_shell.hdf5")
im = si_EDS.get_lines_intensity()
hs.plot.plot_images(
im, tight_layout=True, cmap='RdYlBu_r', axes_decor='off',
colorb... | hyperspy/tests/drawing/test_plot_image.ipynb | vidartf/hyperspy | gpl-3.0 |
Transform Data
Dataset values are all of type "object" => convert to numeric types.
Label Encoder - replaces strings with an incrementing integer. | df = pd.DataFrame(dataset_part.data)
df.head(1)
df = df.apply(pd.to_numeric, errors='ignore')
# Example from http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
'''
le = preprocessing.LabelEncoder()
le.fit(list(names))
# le.classes_ # Shows all labels.
print(le.transform([b'icmp... | 2018-06-24-sklearn-KDDCUP99.ipynb | whiterd/Tutorial-Notebooks | mit |
Preprocessing Data | X = df.values
le = preprocessing.LabelEncoder()
y = le.fit_transform(dataset_part.target)
y_dict = dict(zip(y,le.classes_)) # Saved for later lookup.
# Test options and evaluation metric
N_SPLITS = 7
SCORING = 'accuracy'
# Split-out validation dataset
test_size=0.33
SEED = 42
X_train, X_test, y_train, y_test = trai... | 2018-06-24-sklearn-KDDCUP99.ipynb | whiterd/Tutorial-Notebooks | mit |
Train Model | # Algorithms
models = [
#('LR', LogisticRegression()),
('LDA', LinearDiscriminantAnalysis()),
#('KNN', KNeighborsClassifier()),
#('KMN', KMeans()),
#('CART', DecisionTreeClassifier()),
#('NB', GaussianNB()),
]
# evaluate each model in turn
results =... | 2018-06-24-sklearn-KDDCUP99.ipynb | whiterd/Tutorial-Notebooks | mit |
Use model to make predictions: | test = [0, 1, 22, 9, 181, 5450, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 9, 9, 1.0, 0.0, 0.11, 0.0, 0.0, 0.0, 0.0, 0.0]
print(neigh.predict([test]))
print(neigh.predict_proba([test])) # TODO: research this. | 2018-06-24-sklearn-KDDCUP99.ipynb | whiterd/Tutorial-Notebooks | mit |
Sources
[1] - KDD Cup 99 dataset
[2] - M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009. link
Other Resources
PySpark solution to the KDDCup99
link
... | print('{:10}{:10}{:10}'.format('Model','mean','std'))
print('LDA: 99.49% (0.05%)')
print('{:8}{:^8}{:^8}'.format('Model','mean','std'))
print('-' * 23)
print('{:8}{:^8.2%}{:^8.2%}'.format('LDA', .9949, .0005)) | 2018-06-24-sklearn-KDDCUP99.ipynb | whiterd/Tutorial-Notebooks | mit |
Leggere un file csv con Pandas
Un file csv è un file composto di record di campi separati da , di cui il primo è il record di intestazione che specifica il nome di ognuno dei campi dei record seguenti che contengono i dati.
Funzione read_csv() per leggere un file csv:
df = pd.read_csv(csv_file_name)
df è il riferiment... | df = pd.read_csv("./2017-german-election-overall.csv")
type(df)
df | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ottenere informazioni sul data frame
informazioni generali sul data frame
df.info()
statistiche generali sul data frame
df.describe() | df.info()
df.describe() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ottenere la copia di un data frame
df.copy() | df.copy() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Variabili shape e columns
shape, tupla contenente il numero di righe e numero di colonne del data frame
columns, oggetto Index che contiene i nomi delle colonne del data frame | df.shape
list(df.columns) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Cambiare i nomi delle colonne
df.rename(columns = name_dict, inplace = True|False)
name_dict, dizionario che mappa un nome a un nuovo nome | df.rename(columns = {'registered.voters':'registered_voters', 'area_names':'area'}, inplace = True)
df | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Rimuovere colonne
df.drop(column_list, axis = 1, inplace = True|False) | df.drop(['invalid_second_votes', 'valid_second_votes'], axis=1, inplace = True)
df | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Rimuovere righe per indice
df.drop(index_list, axis = 0, inplace = True|False) | df.drop([7,9,12], axis=0, inplace = True)
df | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ottenere le prime/ultime righe
df.head(n)
df.tail(n) | df.head(8)
df.tail(8)
df.head()
df.tail() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Selezionare righe per posizione (slicing)
df[start_pos:end_pos] | df[0:11]
df[:11]
df.head(11) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Selezionare una colonna
L'espressione:
df[column_name]
restituisce la colonna con nome column_name in un oggetto Series, attraverso cui si possono applicare metodi come max(), min(), count(), var(), std(), mean() etc. oppure describe(). | type(df['registered_voters'])
df['registered_voters']
df['registered_voters'].describe() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
In alternativa si può usare la notazione con il punto:
df.column_name | df.registered_voters.describe() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Selezionare colonne
L'espressione:
df[column_list]
restituisce un data frame con le colonne specificate in column_list, attraverso cui si possono applicare metodi come max(), min(), count(), var(), std(), mean(), corr() etc. | type(df[['registered_voters', 'total_votes']])
df[['registered_voters', 'total_votes']].mean()
df[['registered_voters', 'total_votes']].mean()[1]
df[['registered_voters', 'total_votes']].corr() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
NB: i metodi possono anche essere invocati sul dataset intero. | df.mean()
df.corr() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Controllare se ci sono valori nulli
Le espressioni:
pd.isnull(df)
df.isnull()
restituiscono un data frame di valori booleani. | pd.isnull(df)
df.isnull() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Le espressioni:
pd.isnull(series_obj)
series_obj.isnull()
restituiscono un data frame di valori booleani. | df['state'].isnull()
pd.isnull(df['state']) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Metodo unique()
Il metodo unique() degli oggetti Series restituisce l'array dei valori distinti presenti nell'oggetto invocante. | df['state'].unique()
df.state.unique() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Selezionare le righe che verificano una certa condizione
Le istruzioni equivalenti:
mask = df[column_name] cfr_op value
mask = df.column_name cfr_op value
dove cfr_op è un operatore di confronto, assegnano alla variabile mask un oggetto Series di valori booleani in cui l'i-esimo booleano è True se il valore nell'i-esi... | mask = df['state'] == 'Berlin'
mask
mask = df.state == 'Berlin'
mask | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
L'espressione:
df[mask]
restituisce un data frame con le sole righe che corrispondono a un valore True in mask. | df[mask]
mask = (df['state'] == 'Berlin') | (df['state'] == 'Bayern')
df[mask]
df[mask][['area', 'registered_voters']] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ottenere gli indici delle righe che verificano una certa condizione
df[mask].index | df[mask][['area', 'registered_voters']].index | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Localizzare righe con iloc[]
L'espressione:
df.iloc[pos_index]
restituisce in un oggetto di tipo Series la riga in posizione di indice pos_index. | df.iloc[7]
df.iloc[7]['area'] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
L'espressione:
df.iloc[start_pos_index:end_pos_index]
restituisce in un oggetto di tipo DataFrame tutte le righe dalla posizione di indice start_pos_index a quella di indice end_pos_index-1. | df.iloc[7:12]
df.iloc[7:12]['area'] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
L'espressione:
df.iloc[pos_index_list]
restituisce in un oggetto di tipo DataFrame tutte le righe dalla posizione di indice start_pos_index a quella di indice end_pos_index-1. | df.iloc[[7, 8, 11, 13]]
df.iloc[[7, 8, 11, 13]]['area'] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Uso di loc[]
accesso a una riga tramite il suo indicedf.loc[index] | df.loc[5] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
accesso a più righe tramite i loro indicidf.loc[[index1, index2, ...]] | df.loc[[5,8,10]] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
accesso a un valore del data framedf.loc[index, column_name] | df.loc[5, 'state'] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
accesso a più valori del data framedf.loc[[index1, index2, ...], column_name] | df.loc[[5,10,11], 'state']
df.loc[[5,10,11], 'state'] = 'unknown' | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
accesso a più valori del data framedf.loc[[index1, index2, ...], [column_name1, column_name2, ...]] | df.loc[[5,10,11], ['area', 'state']] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
accesso alle righe che verificano una certa condizionedf.loc[mask] | df.loc[df['state'] == 'Berlin'] | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ottenere un valore tramite un indice con at[]
df.at[index, column_name] | df.at[11, 'area']
df.at[11, 'area'] = 'unknown' | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Ordinare valori
Ordinare per valori di una colonna:
df.sort_values(column_name, ascending = True|False, inplace = True|False)
Ordinare per valori di più colonne:
df.sort_values(column_list, ascending = True|False, inplace = True|False) | df.sort_values('total_votes', ascending = False)
df.sort_values(['state', 'area'], ascending = True) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Raggruppare i valori
L'espressione:
df.groupby(column_name)
df.groupby(column_list)
restituisce un oggetto DataFrameGroupBy. | df.groupby('state')['registered_voters'].sum()
df.groupby(['state', 'area'])['registered_voters'].sum() | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Aggiungere una colonna
df[new_column] = new_series_obj | df['difference'] = df['valid_first_votes'] - df['invalid_first_votes']
df | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Applicare una funzione a un oggetto Series
L'espressione:
series_obj.apply(fun)
applica la funzione fun a tutti i valori in series_obj e restituisce un altro oggetto di tipo Series. | df['registered_voters'].apply(lambda x: float(x+1)) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Applicare una funzione a un oggetto DataFrame
L'espressione:
df.applymap(fun)
applica la funzione fun a tutti i valori in df e restituisce un altro oggetto di tipo DataFrame. | df[['registered_voters', 'total_votes']].applymap(lambda x: 'votes='+str(x)) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Come iterare i record di un data frame
for (index, record) in df.iterrows():
do_something | for (index, record) in df.iterrows():
print(str(index) + ' ' + record['state']) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Scrivere un data frame su un file in formato csv
df.to_csv(file_name, index=False|True) | df.to_csv('./output.csv', index = False) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Richiamare matplotlib da Pandas | df.registered_voters.plot(label="Registered voters", legend=True) | laboratorio/lezione11-04nov21/lezione6-pandas.ipynb | bioinformatica-corso/lezioni | cc0-1.0 |
Kindly ignore the deprecation warnings and incompatibility errors.
Restart the kernel before proceeding further (On the Notebook menu, select Kernel > Restart Kernel > Restart).
Start by importing the necessary libraries for this lab. | # Importing necessary modules/libraries such as numpy, pandas and datetime.
import datetime
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import aiplatform
from matplotlib import pyplot as plt
from tensorflow import feature_column as fc
from tensorflow impor... | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Use tf.data to read the CSV files
We wrote these functions for reading data from the csv files above in the previous notebook. For this lab we will also include some additional engineered features in our model. In particular, we will compute the difference in latitude and longitude, as well as the Euclidean distance be... | # Selecting specific CSV_COLUMNS, LABEL_COLUMN, DEFAULTS, UNWANTED_COLS.
CSV_COLUMNS = [
'fare_amount',
'pickup_datetime',
'pickup_longitude',
'pickup_latitude',
'dropoff_longitude',
'dropoff_latitude',
'passenger_count',
'key'
]
LABEL_COLUMN = 'fare_amount'
DEFAULTS = [[0.0], ['na'], [0... | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Then, we'll define our custom RMSE evaluation metric and build our wide and deep model. | # Here, tf.reduce_mean computes the mean of elements across dimensions of a tensor.
# tf.sqrt Computes element-wise square root of the input tensor.
# tf.square computes square of x element-wise.
def rmse(y_true, y_pred):
return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))
# TODO 3
def build_model(dnn_hidde... | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Next, we can call the build_model to create the model. Here we'll have two hidden layers, each with 10 neurons, for the deep part of our model. We can also use plot_model to see a diagram of the model we've created. | HIDDEN_UNITS = [10,10]
# Calling the build model
model = build_model(dnn_hidden_units=HIDDEN_UNITS)
# Converts a Keras plot_model to see a diagram of the model that we have created.
tf.keras.utils.plot_model(model, show_shapes=False, rankdir='LR') | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Next, we'll set up our training variables, create our datasets for training and validation, and train our model.
(We refer you the the blog post ML Design Pattern #3: Virtual Epochs for further details on why express the training in terms of NUM_TRAIN_EXAMPLES and NUM_EVALS and why, in this training code, the number of... | BATCH_SIZE = 1000
NUM_TRAIN_EXAMPLES = 10000 * 5 # training dataset will repeat, wrap around
NUM_EVALS = 50 # how many times to evaluate
NUM_EVAL_EXAMPLES = 10000 # enough to get a reasonable sample
trainds = create_dataset(
pattern='../data/taxi-train*',
batch_size=BATCH_SIZE,
mode='train')
evalds = c... | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Just as before, we can examine the history to see how the RMSE changes through training on the train set and validation set. | RMSE_COLS = ['rmse', 'val_rmse']
# Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).
pd.DataFrame(history.history)[RMSE_COLS].plot() | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/4_keras_functional_api.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Escribir aquí el valor de la longitud de onda seleccionada (que aparece arriba)
longitud de onda $\lambda_0$= nm
Calculamos el espesor más pequeño de la monocapa escribir aquí su valor numérico modificando este texto
espesor1 = nm
Tarea 3. Caracterización del tratamiento antirreflejante en incidencia normal
V... | # MODIFICAR LOS DOS PARAMETROS. LUEGO EJECUTAR
########################################################
nL = 1.8 # Incluir el índice de la lente de alto índice
espesor1 = 99.0 # Incluir el valor del espesor de la capa (en nm)
# DESDE AQUÍ NO TOCAR
####################################################################... | TratamientoAntirreflejante/Tratamiento_Antirreflejante_Ejercicio.ipynb | ecabreragranado/OpticaFisicaII | gpl-3.0 |
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