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Pandas DataFrame | HTML(table.dframe().head().to_html()) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | e2a55a08a85ab3650b0cd72de889e6b9 |
Dataset dictionary | table.columns() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | d6cfb90a777d30b945a28f2c6724f00b |
Creating tabular data from Elements using the .table and .dframe methods
If you have data in some other HoloViews element and would like to use the columnar data features, you can easily tabularize any of the core Element types into a Table Element, using the .table() method. Similarly, the .dframe() method will convert an Element into a pandas DataFrame. These methods are very useful if you want to then transform the data into a different Element type, or to perform different types of analysis.
Tabularizing simple Elements
For a simple example, we can create a Curve of an exponential function and convert it to a Table with the .table method, with the same result as creating the Table directly from the data as done earlier on this Tutorial: | xs = np.arange(10)
curve = hv.Curve(zip(xs, np.exp(xs)))
curve * hv.Scatter(zip(xs, curve)) + curve.table() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | bd0971f29c2db153508336255f094051 |
Similarly, we can get a pandas dataframe of the Curve using curve.dframe(). Here we wrap that call as raw HTML to allow automated testing of this notebook, but just calling curve.dframe() would give the same result visually: | HTML(curve.dframe().to_html()) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | bbed9b3e2239fa947c78191dd6cf3be5 |
Although 2D image-like objects are not inherently well suited to a flat columnar representation, serializing them by converting to tabular data is a good way to reveal the differences between Image and Raster elements. Rasters are a very simple type of element, using array-like integer indexing of rows and columns from their top-left corner as in computer graphics applications. Conversely, Image elements are a higher-level abstraction that provides a general-purpose continuous Cartesian coordinate system, with x and y increasing to the right and upwards as in mathematical applications, and each point interpreted as a sample representing the pixel in which it is located (and thus centered within that pixel). Given the same data, the .table() representation will show how the data is being interpreted (and accessed) differently in the two cases (as explained in detail in the Continuous Coordinates Tutorial): | %%opts Points (s=200) [size_index=None]
extents = (-1.6,-2.7,2.0,3)
np.random.seed(42)
mat = np.random.rand(3, 3)
img = hv.Image(mat, bounds=extents)
raster = hv.Raster(mat)
img * hv.Points(img) + img.table() + \
raster * hv.Points(raster) + raster.table() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 66ea36ae538d0c63e7d2ca6487e7b84e |
Tabularizing space containers
Even deeply nested objects can be deconstructed in this way, serializing them to make it easier to get your raw data out of a collection of specialized Element types. Let's say we want to make multiple observations of a noisy signal. We can collect the data into a HoloMap to visualize it and then call .table() to get a columnar object where we can perform operations or transform it to other Element types. Deconstructing nested data in this way only works if the data is homogenous. In practical terms, the requirement is that your data structure contains Elements (of any types) in these Container types: NdLayout, GridSpace, HoloMap, and NdOverlay, with all dimensions consistent throughout (so that they can all fit into the same set of columns).
Let's now go back to the Image example. We will now collect a number of observations of some noisy data into a HoloMap and display it: | obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=(0,0,3,3))
for i in range(3)}, key_dimensions=['Observation'])
obs_hmap | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | bcd437ab8cf800ccf67fdaf368f016fa |
Now we can serialize this data just as before, where this time we get a four-column (4D) table. The key dimensions of both the HoloMap and the Images, as well as the z-values of each Image, are all merged into a single table. We can visualize the samples we have collected by converting it to a Scatter3D object. | %%opts Layout [fig_size=150] Scatter3D [color_index=3 size_index=None] (cmap='hot' edgecolor='k' s=50)
obs_hmap.table().to.scatter3d() + obs_hmap.table() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 005e9a2924942c9a04ee612f95db45ca |
Here the z dimension is shown by color, as in the original images, and the other three dimensions determine where the datapoint is shown in 3D. This way of deconstructing will work for any data structure that satisfies the conditions described above, no matter how nested. If we vary the amount of noise while continuing to performing multiple observations, we can create an NdLayout of HoloMaps, one for each level of noise, and animated by the observation number. | from itertools import product
extents = (0,0,3,3)
error_hmap = hv.HoloMap({(i, j): hv.Image(j*np.random.randn(3, 3), bounds=extents)
for i, j in product(range(3), np.linspace(0, 1, 3))},
key_dimensions=['Observation', 'noise'])
noise_layout = error_hmap.layout('noise')
noise_layout | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 08dbafe68aa6606b5d5486f9e9ca44c1 |
Applying operations to the data
Sorting by columns
Once data is in columnar form, it is simple to apply a variety of operations. For instance, Dataset can be sorted by their dimensions using the .sort() method. By default, this method will sort by the key dimensions, but any other dimension(s) can be supplied to specify sorting along any other dimensions: | bars = hv.Bars((['C', 'A', 'B', 'D'], [2, 7, 3, 4]))
bars + bars.sort() + bars.sort(['y']) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 16bf42b19dcfcbcdf43e38cae8bc2154 |
Working with categorical or grouped data
Data is often grouped in various ways, and the Dataset interface provides various means to easily compare between groups and apply statistical aggregates. We'll start by generating some synthetic data with two groups along the x-axis and 4 groups along the y axis. | n = np.arange(1000)
xs = np.repeat(range(2), 500)
ys = n%4
zs = np.random.randn(1000)
table = hv.Table((xs, ys, zs), kdims=['x', 'y'], vdims=['z'])
table | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | cc656b3a4869dbea690c78ea7fed86ef |
Since there are repeat observations of the same x- and y-values, we have to reduce the data before we display it or else use a datatype that supports plotting distributions in this way. The BoxWhisker type allows doing exactly that: | %%opts BoxWhisker [aspect=2 fig_size=200 bgcolor='w']
hv.BoxWhisker(table) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 4d5bab7613af69cdc74721ba2750a8ca |
Aggregating/Reducing dimensions
Most types require the data to be non-duplicated before being displayed. For this purpose, HoloViews makes it easy to aggregate and reduce the data. These two operations are simple inverses of each other--aggregate computes a statistic for each group in the supplied dimensions, while reduce combines all the groups except the supplied dimensions. Supplying only a function and no dimensions will simply aggregate or reduce all available key dimensions. | %%opts Bars [show_legend=False] {+axiswise}
hv.Bars(table).aggregate(function=np.mean) + hv.Bars(table).reduce(x=np.mean) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 89057aa8e06f934c69a596cd1bf97742 |
(A) aggregates over both the x and y dimension, computing the mean for each x/y group, while (B) reduces the x dimension leaving just the mean for each group along y.
Collapsing multiple Dataset Elements
When multiple observations are broken out into a HoloMap they can easily be combined using the collapse method. Here we create a number of Curves with increasingly larger y-values. By collapsing them with a function and a spreadfn we can compute the mean curve with a confidence interval. We then simply cast the collapsed Curve to a Spread and Curve Element to visualize them. | hmap = hv.HoloMap({i: hv.Curve(np.arange(10)*i) for i in range(10)})
collapsed = hmap.collapse(function=np.mean, spreadfn=np.std)
hv.Spread(collapsed) * hv.Curve(collapsed) + collapsed.table() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 81824b31aec5facb1bedc107838dc3ec |
Working with complex data
In the last section we only scratched the surface of what the Dataset interface can do. When it really comes into its own is when working with high-dimensional datasets. As an illustration, we'll load a dataset of some macro-economic indicators for OECD countries from 1964-1990, cached on the HoloViews website. | macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t')
dimensions = {'unem': 'Unemployment',
'capmob': 'Capital Mobility',
'gdp': 'GDP Growth',
'trade': 'Trade',
'year': 'Year',
'country': 'Country'}
macro_df = macro_df.rename(columns=dimensions) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | be3d112728de4bd7f0e3be4ee00ea05c |
We'll also take this opportunity to set default options for all the following plots. | %output dpi=100
options = hv.Store.options()
opts = hv.Options('plot', aspect=2, fig_size=250, show_frame=False, show_grid=True, legend_position='right')
options.NdOverlay = opts
options.Overlay = opts | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 66d797b01b318554aede9ee3e41539af |
Loading the data
As we saw above, we can supply a dataframe to any Dataset type. When dealing with so many dimensions it would be cumbersome to supply all the dimensions explicitly, but luckily Dataset can easily infer the dimensions from the dataframe itself. We simply supply the kdims, and it will infer that all other numeric dimensions should be treated as value dimensions (vdims). | macro = hv.Table(macro_df, kdims=['Year', 'Country']) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | e820e7d4ef5444dd01590f9a0260c409 |
To get an overview of the data we'll quickly sort it and then view the data for one year. | %%opts Table [aspect=1.5 fig_size=300]
macro = macro.sort()
macro[1988] | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 4525ac9994e9cd739e138c7b10dbe45d |
Most of the examples above focus on converting a Table to simple Element types, but HoloViews also provides powerful container objects to explore high-dimensional data, such as HoloMap, NdOverlay, NdLayout, and GridSpace. HoloMaps work as a useful interchange format from which you can conveniently convert to the other container types using its .overlay(), .layout(), and .grid() methods. This way we can easily create an overlay of GDP Growth curves by year for each country. Here Year is a key dimension and GDP Growth a value dimension. We are then left with the Country dimension, which we can overlay using the .overlay() method. | %%opts Curve (color=Palette('Set3'))
gdp_curves = macro.to.curve('Year', 'GDP Growth')
gdp_curves.overlay('Country') | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 2177fab1299f17d481d7b657a1951b85 |
Now that we've extracted the gdp_curves, we can apply some operations to them. As in the simpler example above we will collapse the HoloMap of Curves using a number of functions to visualize the distribution of GDP Growth rates over time. First we find the mean curve with np.std as the spreadfn and cast the result to a Spread type, then we compute the min, mean and max curve in the same way and put them all inside an Overlay. | %%opts Overlay [bgcolor='w' legend_position='top_right'] Curve (color='k' linewidth=1) Spread (facecolor='gray' alpha=0.2)
hv.Spread(gdp_curves.collapse('Country', np.mean, np.std), label='std') *\
hv.Overlay([gdp_curves.collapse('Country', fn).relabel(name)(style=dict(linestyle=ls))
for name, fn, ls in [('max', np.max, '--'), ('mean', np.mean, '-'), ('min', np.min, '--')]]) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | edae7f5cf99d46d3469f932c0c327911 |
Many HoloViews Element types support multiple kdims, including HeatMap, Points, Scatter, Scatter3D, and Bars. Bars in particular allows you to lay out your data in groups, categories and stacks. By supplying the index of that dimension as a plotting option you can choose to lay out your data as groups of bars, categories in each group, and stacks. Here we choose to lay out the trade surplus of each country with groups for each year, no categories, and stacked by country. Finally, we choose to color the Bars for each item in the stack. | %opts Bars [bgcolor='w' aspect=3 figure_size=450 show_frame=False]
%%opts Bars [category_index=2 stack_index=0 group_index=1 legend_position='top' legend_cols=7 color_by=['stack']] (color=Palette('Dark2'))
macro.to.bars(['Country', 'Year'], 'Trade', []) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 08a65820ff50da386744502ea4f4175b |
This plot contains a lot of data, and so it's probably a good idea to focus on specific aspects of it, telling a simpler story about them. For instance, using the .select method we can then customize the palettes (e.g. to use consistent colors per country across multiple analyses).
Palettes can customized by selecting only a subrange of the underlying cmap to draw the colors from. The Palette draws samples from the colormap using the supplied sample_fn, which by default just draws linear samples but may be overriden with any function that draws samples in the supplied ranges. By slicing the Set1 colormap we draw colors only from the upper half of the palette and then reverse it. | %%opts Bars [padding=0.02 color_by=['group']] (alpha=0.6, color=Palette('Set1', reverse=True)[0.:.2])
countries = {'Belgium', 'Netherlands', 'Sweden', 'Norway'}
macro.to.bars(['Country', 'Year'], 'Unemployment').select(Year=(1978, 1985), Country=countries) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | cb6e16e172083a6da53f95ab358f2ebc |
Many HoloViews Elements support multiple key and value dimensions. A HeatMap is indexed by two kdims, so we can visualize each of the economic indicators by year and country in a Layout. Layouts are useful for heterogeneous data you want to lay out next to each other.
Before we display the Layout let's apply some styling; we'll suppress the value labels applied to a HeatMap by default and substitute it for a colorbar. Additionally we up the number of xticks that are drawn and rotate them by 90 degrees to avoid overlapping. Flipping the y-axis ensures that the countries appear in alphabetical order. Finally we reduce some of the margins of the Layout and increase the size. | %opts HeatMap [show_values=False xticks=40 xrotation=90 aspect=1.2 invert_yaxis=True colorbar=True]
%opts Layout [figure_size=120 aspect_weight=0.5 hspace=0.8 vspace=0]
hv.Layout([macro.to.heatmap(['Year', 'Country'], value)
for value in macro.data.columns[2:]]).cols(2) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 4a7e03d482447a757d5944afca42294c |
Another way of combining heterogeneous data dimensions is to map them to a multi-dimensional plot type. Scatter Elements, for example, support multiple vdims, which may be mapped onto the color and size of the drawn points in addition to the y-axis position.
As for the Curves above we supply 'Year' as the sole key dimension and rely on the Table to automatically convert the Country to a map dimension, which we'll overlay. However this time we select both GDP Growth and Unemployment, to be plotted as points. To get a sensible chart, we adjust the scaling_factor for the points to get a reasonable distribution in sizes and apply a categorical Palette so we can distinguish each country. | %%opts Scatter [scaling_method='width' scaling_factor=2] (color=Palette('Set3') edgecolors='k')
gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment'])
gdp_unem_scatter.overlay('Country') | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 6d5618ac2bf699167b920deff2cf5a0c |
In this way we can plot any dimension against any other dimension, very easily allowing us to iterate through different ways of revealing relationships in the dataset. | %%opts NdOverlay [legend_cols=2] Scatter [size_index=1] (color=Palette('Blues'))
macro.to.scatter('GDP Growth', 'Unemployment', ['Year']).overlay() | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 060645fab6c432d16d7345e406e01d4e |
This view, for example, immediately highlights the high unemployment rates of the 1980s.
Since all HoloViews Elements are composable, we can generate complex figures just by applying the * operator. We'll simply reuse the GDP curves we generated earlier, combine them with the scatter points (which indicate the unemployment rate by size) and annotate the data with some descriptions of what happened economically in these years. | %%opts Curve (color='k') Scatter [color_index=2 size_index=2 scaling_factor=1.4] (cmap='Blues' edgecolors='k')
macro_overlay = gdp_curves * gdp_unem_scatter
annotations = hv.Arrow(1973, 8, 'Oil Crisis', 'v') * hv.Arrow(1975, 6, 'Stagflation', 'v') *\
hv.Arrow(1979, 8, 'Energy Crisis', 'v') * hv.Arrow(1981.9, 5, 'Early Eighties\n Recession', 'v')
macro_overlay * annotations | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 6b7b25d089ebef1c3cb6f314317c9724 |
Since we didn't map the country to some other container type, we get a widget allowing us to view the plot separately for each country, reducing the forest of curves we encountered before to manageable chunks.
While looking at the plots individually like this allows us to study trends for each country, we may want to lay out a subset of the countries side by side, e.g. for non-interactive publications. We can easily achieve this by selecting the countries we want to view and and then applying the .layout method. We'll also want to restore the square aspect ratio so the plots compose nicely. | %opts Overlay [aspect=1]
%%opts NdLayout [figure_size=100] Scatter [color_index=2] (cmap='Reds')
countries = {'United States', 'Canada', 'United Kingdom'}
(gdp_curves * gdp_unem_scatter).select(Country=countries).layout('Country') | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | e975a4249a45860d9de3875b6fc7be1f |
Finally, let's combine some plots for each country into a Layout, giving us a quick overview of each economic indicator for each country: | %%opts Layout [fig_size=100] Scatter [color_index=2] (cmap='Reds')
(macro_overlay.relabel('GDP Growth', depth=1) +\
macro.to.curve('Year', 'Unemployment', ['Country'], group='Unemployment',) +\
macro.to.curve('Year', 'Trade', ['Country'], group='Trade') +\
macro.to.scatter('GDP Growth', 'Unemployment', ['Country'])).cols(2) | doc/Tutorials/Columnar_Data.ipynb | vascotenner/holoviews | bsd-3-clause | 7c3365a1cd6f6e76dea93f0669b39102 |
Find maximum of Bernoulli distribution
Single experiment
$$\phi(x) = p ^ {x} * (1 - p) ^ { 1 - x }$$
Series of experiments
$$\mathcal{L}(p|x) = \prod_{i=1}^{n} p^{x_{i}}*(p-1)^{1-x_{i}}$$
Hints
sympy.diff()
sympy.expand()
sympy.expand_log()
sympy.solve()
sympy.symbols()
sympy gotchas | import sympy
from sympy.abc import x
p = sympy.symbols('p', positive=True)
phi = p ** x * (1 - p) ** (1 - x)
L = np.prod([phi.subs(x, i) for i in xs]) # objective function to maximize
log_L = sympy.expand_log(sympy.log(L))
sol = sympy.solve(sympy.diff(log_L, p), p)[0]
import matplotlib.pyplot as plt
x_space = np.linspace(1/100, 1, 100, endpoint=False)
plt.plot(x_space,
list(map(sympy.lambdify(p, log_L, 'numpy'), x_space)),
sol,
log_L.subs(p, sol),
'o',
p_true,
log_L.subs(p, p_true),
's',
)
plt.xlabel('$p$', fontsize=18)
plt.ylabel('Likelihood', fontsize=18)
plt.title('Estimate not equal to true value', fontsize=18)
plt.grid(True)
plt.show() | mle.ipynb | hyzhak/mle | mit | 7f2aa1e2b57622f25a1f154d003b0127 |
Empirically examine the behavior of the maximum likelihood estimator
evalf() | def estimator_gen(niter=10, ns=100):
"""
generate data to estimate distribution of maximum likelihood estimator'
"""
x = sympy.symbols('x', real=True)
phi = p**x*(1-p)**(1-x)
for i in range(niter):
xs = sample(ns) # generate some samples from the experiment
L = np.prod([phi.subs(x,i) for i in xs]) # objective function to maximize
log_L = sympy.expand_log(sympy.log(L))
sol = sympy.solve(sympy.diff(log_L, p), p)[0]
yield float(sol.evalf())
entries = list(estimator_gen(100)) # this may take awhile, depending on how much data you want to generate
plt.hist(entries) # histogram of maximum likelihood estimator
plt.title('$\mu={:3.3f},\sigma={:3.3f}$'.format(np.mean(entries), np.std(entries)), fontsize=18)
plt.show() | mle.ipynb | hyzhak/mle | mit | e41afa8c6d85d59a675120a98ade61b2 |
Dynamic of MLE by length sample sequence | def estimator_dynamics(ns_space, num_tries = 20):
for ns in ns_space:
estimations = list(estimator_gen(num_tries, ns))
yield np.mean(estimations), np.std(estimations)
ns_space = list(range(10, 100, 5))
entries = list(estimator_dynamics(ns_space))
entries_mean = list(map(lambda e: e[0], entries))
entries_std = list(map(lambda e: e[1], entries))
plt.errorbar(ns_space, entries_mean, entries_std, fmt='-o')
plt.show() | mle.ipynb | hyzhak/mle | mit | 175c199d6f3bdce610039efd699da540 |
I'll fit a significantly larger vocabular this time, as the embeddings are basically given for us. | num_words = 5000
max_len = 400
tok = Tokenizer(num_words)
tok.fit_on_texts(input_text[:25000])
X_train = tok.texts_to_sequences(input_text[:25000])
X_test = tok.texts_to_sequences(input_text[25000:])
y_train = input_label[:25000]
y_test = input_label[25000:]
X_train = sequence.pad_sequences(X_train, maxlen=max_len)
X_test = sequence.pad_sequences(X_test, maxlen=max_len)
words = []
for iter in range(num_words):
words += [key for key,value in tok.word_index.items() if value==iter+1]
loc = "/Users/taylor/files/word2vec_python/GoogleNews-vectors-negative300.bin"
w2v = word2vec.Word2Vec.load_word2vec_format(loc, binary=True)
weights = np.zeros((num_words,300))
for idx, w in enumerate(words):
try:
weights[idx,:] = w2v[w]
except KeyError as e:
pass
model = Sequential()
model.add(Embedding(num_words, 300, input_length=max_len))
model.add(Dropout(0.5))
model.add(GRU(16,activation='relu'))
model.add(Dense(128))
model.add(Dropout(0.5))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.layers[0].set_weights([weights])
model.layers[0].trainable = False
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=32, nb_epoch=10, verbose=1,
validation_data=(X_test, y_test)) | lectures/lec22/.ipynb_checkpoints/notebook22-checkpoint.ipynb | statsmaths/stat665 | gpl-2.0 | 6967f0e9efcac1acb666b23712af5843 |
Session 1.4
Collections Sets and dictionaries | # Sets
my_set = set([1, 2, 3, 3, 3, 4])
print(my_set)
len(my_set)
my_set.add(3) # sets are unordered
print(my_set)
my_set.remove(3)
print(my_set)
# set operation using union | or intersection &
my_first_set = set([1, 2, 4, 6, 8])
my_second_set = set([8, 9, 10])
my_first_set | my_second_set
my_first_set & my_second_set | live/python_basic_1_4_live.ipynb | pycam/python-basic | unlicense | 88c1a5cf003fcad52dc1eae8b11ea93d |
Exercises 1.4.1
Given the protein sequence "MPISEPTFFEIF", find the unique amino acids in the sequence. | # Dictionnaries are collections of key/value pairs
my_dict = {'A': 'Adenine', 'C': 'Cytosine', 'T': 'Thymine', 'G': 'Guanine'}
print(my_dict)
my_dict['C']
my_dict['N']
?my_dict.get
my_dict.get('N', 'unknown')
print(my_dict)
len(my_dict)
type(my_dict)
'T' in my_dict
# Assign new key/value pair
my_dict['Y'] = 'Pyrimidine'
print(my_dict)
my_dict['Y'] = 'Cytosine or Thymine'
print(my_dict)
del my_dict['Y']
print(my_dict)
help(dict)
my_dict.keys()
list(my_dict.keys())
my_dict.values()
my_dict.items() | live/python_basic_1_4_live.ipynb | pycam/python-basic | unlicense | 7fa3f517ce9e760c0846fb27cf99449a |
To begin, let's make a function which will create $N$ noisy, irregularly-spaced data points containing a periodic signal, and plot one realization of that data: | def create_data(N, period=2.5, err=0.1, rseed=0):
rng = np.random.RandomState(rseed)
t = np.arange(N, dtype=float) + 0.3 * rng.randn(N)
y = np.sin(2 * np.pi * t / period) + err * rng.randn(N)
return t, y, err
t, y, dy = create_data(100, period=20)
plt.errorbar(t, y, dy, fmt='o'); | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | fbebb1765beebdc435d04ad08bc8bf65 |
From this, our algorithm should be able to identify any periodicity that is present.
Choosing the Frequency Grid
The Lomb-Scargle Periodogram works by evaluating a power for a set of candidate frequencies $f$. So the first question is, how many candidate frequencies should we choose?
It turns out that this question is very important. If you choose the frequency spacing poorly, it may lead you to miss strong periodic signal in the data!
Frequency spacing
First, let's think about the frequency spacing we need in our grid. If you're asking about a candidate frequency $f$, then data with range $T$ contains $T \cdot f$ complete cycles. If our error in frequency is $\delta f$, then $T\cdot\delta f$ is the error in number of cycles between the endpoints of the data.
If this error is a significant fraction of a cycle, this will cause problems. This givs us the criterion
$$
T\cdot\delta f \ll 1
$$
Commonly, we'll choose some oversampling factor around 5 and use $\delta f = (5T)^{-1}$ as our frequency grid spacing.
Frequency limits
Next, we need to choose the limits of the frequency grid. On the low end, $f=0$ is suitable, but causes some problems – we'll go one step away and use $\delta f$ as our minimum frequency.
But on the high end, we need to make a choice: what's the highest frequency we'd trust our data to be sensitive to?
At this point, many people are tempted to mis-apply the Nyquist-Shannon sampling theorem, and choose some version of the Nyquist limit for the data.
But this is entirely wrong! The Nyquist frequency applies for regularly-sampled data, but irregularly-sampled data can be sensitive to much, much higher frequencies, and the upper limit should be determined based on what kind of signals you are looking for.
Still, a common (if dubious) rule-of-thumb is that the high frequency is some multiple of what Press & Rybicki call the "average" Nyquist frequency,
$$
\hat{f}_{Ny} = \frac{N}{2T}
$$
With this in mind, we'll use the following function to determine a suitable frequency grid: | def freq_grid(t, oversampling=5, nyquist_factor=3):
T = t.max() - t.min()
N = len(t)
df = 1. / (oversampling * T)
fmax = 0.5 * nyquist_factor * N / T
N = int(fmax // df)
return df + df * np.arange(N) | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | 6ade5ec8a7b3c3c50dfaa092a49deb59 |
Now let's use the gatspy tools to plot the periodogram: | t, y, dy = create_data(100, period=2.5)
freq = freq_grid(t)
print(len(freq))
from gatspy.periodic import LombScargle
model = LombScargle().fit(t, y, dy)
period = 1. / freq
power = model.periodogram(period)
plt.plot(period, power)
plt.xlim(0, 5); | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | de020f312d556570370a1afdc81f89cb |
The algorithm finds a strong signal at a period of 2.5.
To demonstrate explicitly that the Nyquist rate doesn't apply in irregularly-sampled data, let's use a period below the averaged sampling rate and show that we can find it: | t, y, dy = create_data(100, period=0.3)
period = 1. / freq_grid(t, nyquist_factor=10)
model = LombScargle().fit(t, y, dy)
power = model.periodogram(period)
plt.plot(period, power)
plt.xlim(0, 1); | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | 5ff69bc472abcdcbfa0bbf735d81e90d |
With a data sampling rate of approximately $1$ time unit, we easily find a period of $0.3$ time units. The averaged Nyquist limit clearly does not apply for irregularly-spaced data!
Nevertheless, short of a full analysis of the temporal window function, it remains a useful milepost in estimating the upper limit of frequency.
Scaling with $N$
With these rules in mind, we see that the size of the frequency grid is approximately
$$
N_f = \frac{f_{max}}{\delta f} \propto \frac{N/(2T)}{1/T} \propto N
$$
So for $N$ data points, we will require some multiple of $N$ frequencies (with a constant of proportionality typically on order 10) to suitably explore the frequency space.
This is the source of the $N^2$ scaling of the typical periodogram: finding periods in $N$ datapoints requires a grid of $\sim 10N$ frequencies, and $O[N^2]$ operations.
When $N$ gets very, very large, this becomes a problem.
Fast Periodograms with LombScargleFast
Finally we get to the meat of this discussion.
In a 1989 paper, Press and Rybicki proposed a clever method whereby a Fast Fourier Transform is used on a grid extirpolated from the original data, such that this problem can be solved in $O[N\log N]$ time. The gatspy package contains a pure-Python implementation of this algorithm, and we'll explore it here.
If you're interested in seeing how the algorithm works in Python, check out the code in the gatspy source.
It's far more readible and understandable than the Fortran source presented in Press et al.
For convenience, the implementation has a periodogram_auto method which automatically selects a frequency/period range based on an oversampling factor and a nyquist factor: | from gatspy.periodic import LombScargleFast
help(LombScargleFast.periodogram_auto)
from gatspy.periodic import LombScargleFast
t, y, dy = create_data(100)
model = LombScargleFast().fit(t, y, dy)
period, power = model.periodogram_auto()
plt.plot(period, power)
plt.xlim(0, 5); | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | 83d0a71920ff1139e198d890791d0a13 |
Here, to illustrate the different computational scalings, we'll evaluate the computational time for a number of inputs, using LombScargleAstroML (a fast implementation of the $O[N^2]$ algorithm) and LombScargleFast, which is the fast FFT-based implementation: | from time import time
from gatspy.periodic import LombScargleAstroML, LombScargleFast
def get_time(N, Model):
t, y, dy = create_data(N)
model = Model().fit(t, y, dy)
t0 = time()
model.periodogram_auto()
t1 = time()
result = t1 - t0
# for fast operations, we should do several and take the median
if result < 0.1:
N = min(50, 0.5 / result)
times = []
for i in range(5):
t0 = time()
model.periodogram_auto()
t1 = time()
times.append(t1 - t0)
result = np.median(times)
return result
N_obs = list(map(int, 10 ** np.linspace(1, 4, 5)))
times1 = [get_time(N, LombScargleAstroML) for N in N_obs]
times2 = [get_time(N, LombScargleFast) for N in N_obs]
plt.loglog(N_obs, times1, label='Naive Implmentation')
plt.loglog(N_obs, times2, label='FFT Implementation')
plt.xlabel('N observations')
plt.ylabel('t (sec)')
plt.legend(loc='upper left'); | examples/FastLombScargle.ipynb | nhuntwalker/gatspy | bsd-2-clause | f60641000b692a8c224e4f8c686fd54a |
Now, let's apply the theory of rotation matrices to write some code which will rotate a vector by amount $\theta$. The function rotmat(th) returns the rotation matrix. | def rotmat(th):
rotator = np.array([[ma.cos(th), -ma.sin(th)],[ma.sin(th), ma.cos(th)]])
return rotator | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | ff58c5f5bb9ac340bc20f482fb02b0f9 |
This function rotation(th, vec) takes in a rotation angle and vector input and returns a tuple of numpy arrays which can be animated to create a "smooth transition" of the rotation using Plotly Animate. | def rotation(th, vec):
# Parameters
t = np.linspace(0,1,50)
tt = th*t
# Rotation matrix
BigR = np.identity(2)
for i in range(len(tt)-1):
BigR = np.vstack((BigR,rotmat(tt[i+1])))
newvec = np.matmul(BigR,vec)
x = newvec[::2]
y = newvec[1::2]
return (x,y) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | bd05deccbc4a8df6a362668c374fa31d |
In the cell below, enter a rotation angle and vector inside the rotation() function which has some inputs inside already and hit shift enter to generate an animation of the rotation! (<b>N.B. Don't worry too much if you're not familiar with the plotly syntax, it's more important you understand what the matrices are doing, the cell will run itself after you choose the input arguments and hit Shift + Enter</b>) | # Enter a 2D vector here...
vec = [1,0]
# Enter rotation angle here...
th = 4
(x0,y0) = rotation(th, vec)
x0 = list(x0)
y0 = list(y0)
# Syntax for plotly, see documentation for more info
data = [{"x": [x0[i],0], "y": [y0[i],0], "frame": i} for i in range(len(x0))]
figure = {'data': [{'x': data[0]['x'], 'y': data[0]['y']}],
'layout': {'xaxis': {'range': [-2, 2], 'autorange': False},
'yaxis': {'range': [-2, 2], 'autorange': False},
'height': 600,
'width': 600,
'title': 'Rotation Animation',
'updatemenus': [{'type': 'buttons',
'buttons': [{'label': 'Play',
'method': 'animate',
'args': [None, dict(frame=dict(duration=50, redraw=False),
transition=dict(duration=50),
fromcurrent=True,
mode='immediate')]}]}]
},
'frames': [{'data': [{'x': data[i]['x'], 'y': data[i]['y']}]} for i in range(len(x0))]
}
# Plot
iplot(figure) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | 373d26913f4d092d35c029e34915f976 |
3. Scaling Matrices
Now we are familiar with rotation matrices, we will move onto another type of matrix transformation known as a "scaling" matrix. Scaling matrices have the form:
<br>
<br>
$$ \text{Scale} = \begin{pmatrix} s1 & 0 \ 0 & s2 \end{pmatrix} $$
<br>
Now let's look at what this matrix does to an arbitrary vector $(x, y)$:
<br><br>
$$ \begin{pmatrix} s1 & 0 \ 0 & s2 \end{pmatrix}\begin{pmatrix} x \ y\end{pmatrix} = s1\begin{pmatrix}x\0\end{pmatrix}+s2\begin{pmatrix}0\y\end{pmatrix}$$
<br>
As we can see, this "scale" matrix scales the vector in the $x$-direction by a factor $s1$ and scales the vector in the $y$-direction by a factor s2. Now we write a function scale(vec, *args) which takes in a vector input as well as an additional 1 OR 2 arguments. If one is given, then a matrix which scales both $x$ and $y$ directions equally is returned while if 2 are given then a matrix which scales by the arguments given is returned. | # Input vector, scale 1, scale 2 as arguments
def scale(vec, *args):
assert len(vec)==2, "Please provide a 2D vector for the first argument"
assert len(args)==1 or len(args)==2, "Please provide 1 or 2 scale arguments"
t = np.linspace(1,args[0],50)
# If only one scale argument given then scale in both directions by same amount
if len(args) == 1:
x = vec[0]*t
y = vec[1]*t
return(x,y)
# If two scale arguments given then scale individual directions
else:
s = np.linspace(1,args[1],50)
x = vec[0]*t
y = vec[1]*s
return(x,y) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | 0d6c51e5f7eca2da1215f879a7a1ff46 |
Now try it for yourself by running the function with your own inputs, by default 2 scale arguments have been inputted but you can try 1 if you like as well. | # Again input vector here
vec = [1,1]
# Arguments here
s1 = 2
s2 = 3
(x1,y1) = scale(vec, s1, s2)
x1 = list(x1)
y1 = list(y1)
# Plotly syntax again
data = [{"x": [x1[i],0], "y": [y1[i],0], "frame": i} for i in range(len(x1))]
figure = {'data': [{'x': data[0]['x'], 'y': data[0]['y']}],
'layout': {'xaxis': {'range': [-2, 2], 'autorange': False},
'yaxis': {'range': [-2, 2], 'autorange': False},
'height': 600,
'width': 600,
'title': 'Scale Animation',
'updatemenus': [{'type': 'buttons',
'buttons': [{'label': 'Play',
'method': 'animate',
'args': [None, dict(frame=dict(duration=50, redraw=False),
transition=dict(duration=50),
fromcurrent=True,
mode='immediate')]}]}]
},
'frames': [{'data': [{'x': data[i]['x'], 'y': data[i]['y']}]} for i in range(len(x1))]
}
iplot(figure) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | edd3cf1392678b84ea9a7a52d0eebfa6 |
4. Custom Matrix
Now we have explained some basic matrix transformations, feel free to use the following code to create your own 2x2 matrix transformations. | # Custom 2D transformation
def custom(vec):
print("Enter values for 2x2 matrix [[a,b],[c,d]] ")
a = input("Enter a value for a: ")
b = input("Enter a value for b: ")
c = input("Enter a value for c: ")
d = input("Enter a value for d: ")
try:
a = float(a)
except ValueError:
print("Enter a float or integer for a")
try:
b = float(b)
except ValueError:
print("Enter a float or integer for b")
try:
c = float(c)
except ValueError:
print("Enter a float or integer for c")
try:
d = float(d)
except ValueError:
print("Enter a float or integer for d")
A = [[a,b],[c,d]]
t = np.linspace(0,1,50)
w = np.matmul(A,vec)-vec
x = [vec[0]+tt*w[0] for tt in t]
y = [vec[1]+tt*w[1] for tt in t]
return(x,y)
(x2,y2) = custom([1,1])
x2 = list(x2)
y2 = list(y2)
data = [{"x": [x2[i],0], "y": [y2[i],0], "frame": i} for i in range(len(x2))]
figure = {'data': [{'x': data[0]['x'], 'y': data[0]['y']}],
'layout': {'xaxis': {'range': [-2, 2], 'autorange': False},
'yaxis': {'range': [-2, 2], 'autorange': False},
'height': 600,
'width': 600,
'title': 'Custom Animation',
'updatemenus': [{'type': 'buttons',
'buttons': [{'label': 'Play',
'method': 'animate',
'args': [None, dict(frame=dict(duration=50, redraw=False),
transition=dict(duration=50),
fromcurrent=True,
mode='immediate')]}]}]
},
'frames': [{'data': [{'x': data[i]['x'], 'y': data[i]['y']}]} for i in range(len(x2))]
}
iplot(figure) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | b636abf978d4f3a01475384d3e60cec6 |
5. Skew Matrices
For the next matrix we will use a slightly different approach to visualize what this transformation does. Instead of taking one vector and following what the matrix does to it, we will take 3 vectors ((1, 0), (1, 1) and (0, 1)) and look at what the matrix does to the entire area captured between these 3 points and the origin (i.e. the unit box). Why is this? <br>
Well, matrix transformations are linear transformations and any point inside the box is a linear combination of $\mathbf{\hat{i}},\,\mathbf{\hat{j}}$ unit vectors. Consider a matrix $A$ acting upon a vector (x,y). <br><br>
$$ A \begin{pmatrix}x\y\end{pmatrix} = \begin{pmatrix}a&b\c&d\end{pmatrix}\begin{pmatrix}x\y\end{pmatrix} =
x\begin{pmatrix}a\c\end{pmatrix}+y\begin{pmatrix}b\d\end{pmatrix}
$$ <br>
As we can see, the $\mathbf{\hat{i}},\,\mathbf{\hat{j}}$ unit vectors are mapped to vectors $(a,\,c)$ and $(b,\,d)$ , respectively, so any points inside the unit square are mapped inside the parallelogram formed by the 2 vectors $(a,\,c)$ and $(b,\,d)$, (see the <b>Parallelipiped</b> visualization for more info). To visualize this, let's write a function which returns a skew matrix and see how it deforms the unit square. It's okay if you're not sure what a skew matrix is or what it does as you'll see what happens when we make the animation. | def skew(axis, vec):
t = np.linspace(0,1,50)
# Skew in x-direction
if axis == 0:
A = [[1,1],[0,1]]
w = np.matmul(A,vec)-vec
x = [vec[0]+tt*w[0] for tt in t]
y = [vec[1]+tt*w[1] for tt in t]
return(x, y)
# Skew in y-direction
elif axis == 1:
A = [[1,0],[1,1]]
w = np.matmul(A,vec)-vec
x = [vec[0]+tt*w[0] for tt in t]
y = [vec[1]+tt*w[1] for tt in t]
return(x, y)
else:
return ValueError('Axis must be 0 or 1') | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | 883bc37c466e6c6c040825306f3dd929 |
Now we write a function which will take 6 arrays in total (2 for (1, 0), 2 for (0, 1) and 2 for (1, 1)) and shows an animation of how the 3 vectors are transformed. Remember that we can forget about the origin as it is always mapped to itself (this is a standard property of linear transformations). | # Function that returns data in a format to be used by plotly and then plots it
def sqtransformation(x0,x1,x2,y0,y1,y2):
data = [{"x": [0,x0[i],x1[i],x2[i],0], "y": [0,y0[i],y1[i],y2[i],0], "frame": i} for i in range(len(x0))]
figure = {'data': [{'x': data[0]['x'], 'y': data[0]['y'], 'fill':'tonexty'}],
'layout': {'xaxis': {'range': [-2, 2], 'autorange': False},
'yaxis': {'range': [-2, 2], 'autorange': False},
'height': 600,
'width': 600,
'title': 'Square Animation',
'updatemenus': [{'type': 'buttons',
'buttons': [{'label': 'Play',
'method': 'animate',
'args': [None, dict(frame=dict(duration=50, redraw=False),
transition=dict(duration=50),
fromcurrent=True,
mode='immediate')]}]}]
},
'frames': [{'data': [{'x': data[i]['x'], 'y': data[i]['y']}]} for i in range(len(x0))]
}
iplot(figure)
# Transform the 3 vectors that form the unit box.
(x0,y0) = skew(1,[1,0])
(x1,y1) = skew(1,[1,1])
(x2,y2) = skew(1,[0,1])
sqtransformation(x0,x1,x2,y0,y1,y2) | visuals_maths/2D_Transformations/notebook/2D_transformations.ipynb | cydcowley/Imperial-Visualizations | mit | 2b7890a8cd4a5d2fa4e3e00d9444db0e |
1. Single Day Analysis | ref_date = '2020-01-02'
engine = SqlEngine(os.environ['DB_URI'])
universe = Universe('hs300')
codes = engine.fetch_codes(ref_date, universe)
total_data = engine.fetch_data(ref_date, 'EMA5D', codes, 300, industry='sw', risk_model='short')
all_styles = risk_styles + industry_styles + ['COUNTRY']
risk_cov = total_data['risk_cov'][all_styles].values
factor = total_data['factor']
risk_exposure = factor[all_styles].values
special_risk = factor['srisk'].values | notebooks/Example 6 - Target Volatility Builder.ipynb | wegamekinglc/alpha-mind | mit | 64882faebe36c19d2d0feaeb3af24d76 |
Portfolio Construction
using EPS factor as alpha factor;
short selling is forbiden;
target of volatility for the activate weight is setting at 2.5% annually level. | er = factor['EMA5D'].fillna(factor["EMA5D"].median()).values
bm = factor['weight'].values
lbound = np.zeros(len(er))
ubound = bm + 0.01
cons_mat = np.ones((len(er), 1))
risk_targets = (bm.sum(), bm.sum())
target_vol = 0.025
risk_model = dict(cov=None, factor_cov=risk_cov/10000, factor_loading=risk_exposure, idsync=special_risk ** 2 / 10000.)
status, p_er, p_weight = \
target_vol_builder(er, risk_model, bm, lbound, ubound, cons_mat, risk_targets, target_vol)
sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000. + np.diag(special_risk ** 2) / 10000
# check the result
print(f"total weight is {p_weight.sum(): .4f}")
print(f"portfolio activate weight forecasting vol is {np.sqrt((p_weight - bm) @ sec_cov @ (p_weight - bm)):.4f}")
print(f"portfolio er: {p_weight @ er:.4f} comparing with benchmark er: {bm @ er:.4f}") | notebooks/Example 6 - Target Volatility Builder.ipynb | wegamekinglc/alpha-mind | mit | 95613eca60d022d38e3423c7c9477e19 |
2. Porfolio Construction: 2016 ~ 2018 | """
Back test parameter settings
"""
start_date = '2020-01-01'
end_date = '2020-02-21'
freq = '10b'
neutralized_risk = industry_styles
industry_name = 'sw'
industry_level = 1
risk_model = 'short'
batch = 0
horizon = map_freq(freq)
universe = Universe('hs300')
data_source = os.environ['DB_URI']
benchmark_code = 300
target_vol = 0.05
weights_bandwidth = 0.02
"""
Factor Model
"""
alpha_factors = {'f01': CSRank(LAST('EMA5D'))}
weights = dict(f01=1.)
alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)
data_meta = DataMeta(freq=freq,
universe=universe,
batch=batch,
neutralized_risk=neutralized_risk,
risk_model='short',
pre_process=[winsorize_normal, standardize],
post_process=[standardize],
warm_start=0,
data_source=data_source)
"""
Constraintes settings
"""
constraint_risk = ['SIZE', 'SIZENL', 'BETA']
total_risk_names = constraint_risk + ['benchmark', 'total']
b_type = []
l_val = []
u_val = []
previous_pos = pd.DataFrame()
rets = []
turn_overs = []
leverags = []
for name in total_risk_names:
if name == 'benchmark':
b_type.append(BoundaryType.RELATIVE)
l_val.append(0.8)
u_val.append(1.0)
else:
b_type.append(BoundaryType.ABSOLUTE)
l_val.append(0.0)
u_val.append(0.0)
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
"""
Running Settings
"""
running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,
rebalance_method='tv',
bounds=bounds,
target_vol=target_vol)
"""
Strategy run
"""
strategy = Strategy(alpha_model,
data_meta,
universe=universe,
start_date=start_date,
end_date=end_date,
freq=freq,
benchmark=benchmark_code)
strategy.prepare_backtest_data()
ret_df, positions = strategy.run(running_setting)
ret_df[['excess_return', 'turn_over']].cumsum().plot(figsize=(14, 7),
title='Fixed freq rebalanced with target vol \
at {2}: {0} with benchmark {1}'.format(freq, benchmark_code, target_vol),
secondary_y='turn_over') | notebooks/Example 6 - Target Volatility Builder.ipynb | wegamekinglc/alpha-mind | mit | 040f422156f18e8de2ddc02a515f4fb4 |
We're going to load the data in using h5py from an hdf5 file. HDF5 is a file format that allows for very simple storage of numerical data; in this particular case, we'll be loading in a 3D array, and then examining it. | f = h5py.File("/srv/nbgrader/data/koala.hdf5", "r")
print(list(f.keys())) | week05/examples_week05.ipynb | UIUC-iSchool-DataViz/spring2017 | mit | 438449a972bb55de35a9576c9a5dc7da |
Here, we load in the data by reading from the key koala that we just found. | koala = f["/koala"][:]
print(koala.shape) | week05/examples_week05.ipynb | UIUC-iSchool-DataViz/spring2017 | mit | 6a4b189eb155ff8b9d7ee5d5ee654162 |
We'll use subplots to show the maximum value along each of the three axes, along with a histogram of all the values. The .max() function here accepts and axis argument, which means "max along a given axis." | for i in range(3):
plt.subplot(2,2,i+1)
plt.imshow(koala.max(axis=i), interpolation='nearest', origin='lower', cmap='viridis')
plt.subplot(2,2,4)
plt.hist(koala.ravel(), bins = 32, log = True) | week05/examples_week05.ipynb | UIUC-iSchool-DataViz/spring2017 | mit | 3e7cde3178ae7ab864fdf06b6ccf9112 |
We'll make a slicer, too -- this one is along the x value. Note how we take a floating point value and turn that into an index to make the image. | def xslicer(coord = 0.5):
# We're accepting a float here, so we convert that into the right index we want
ind = int(coord * koala.shape[0])
plt.imshow(koala[ind,:,:], interpolation = 'nearest', origin='lower')
ipywidgets.interact(xslicer, coord = (0.0, 1.0, 0.01)) | week05/examples_week05.ipynb | UIUC-iSchool-DataViz/spring2017 | mit | bbc75cfd7e71fbc8f068fde78413a866 |
Download the dataset from its repository at github
https://github.com/jeanpat/DeepFISH/tree/master/dataset | !wget https://github.com/jeanpat/DeepFISH/blob/master/dataset/LowRes_13434_overlapping_pairs.h5
filename = './LowRes_13434_overlapping_pairs.h5'
h5f = h5py.File(filename,'r')
pairs = h5f['dataset_1'][:]
h5f.close()
print('dataset is a numpy array of shape:', pairs.shape)
N = 11508
grey = pairs[N,:,:,0]
g_truth = pairs[N,:,:,1]
l1, l2, l3, seg = clean_ground_truth(g_truth, size = 1)
test = np.dstack((grey, g_truth))
print(test.shape)
t2 = np.stack((test,test))
print(t2.shape) | notebooks/Clean Dataset from their spurious pixels.ipynb | jeanpat/DeepFISH | gpl-3.0 | 82a95750922634fc6d2223eb5273f393 |
Let's compare the groundtruth image befor and after cleaning | plt.figure(figsize=(20,10))
plt.subplot(251,xticks=[],yticks=[])
plt.imshow(grey, cmap=plt.cm.gray)
plt.subplot(252,xticks=[],yticks=[])
plt.imshow(g_truth, cmap=plt.cm.flag_r)
plt.subplot(253,xticks=[],yticks=[])
plt.imshow(g_truth == 1, cmap=plt.cm.flag_r)
plt.subplot(254,xticks=[],yticks=[])
plt.imshow(g_truth == 2, cmap=plt.cm.flag_r)
plt.subplot(255,xticks=[],yticks=[])
plt.imshow(g_truth == 3, cmap=plt.cm.flag_r)
#plt.subplot(256,xticks=[],yticks=[])
#plt.imshow(mo.white_tophat(grey, selem = mo.disk(2)) > 0, cmap=plt.cm.jet)
plt.subplot(257,xticks=[],yticks=[])
plt.imshow(l1+2*l2+3*l3, cmap=plt.cm.flag_r)
plt.subplot(258,xticks=[],yticks=[])
plt.imshow(l1, cmap=plt.cm.flag_r)
plt.subplot(259,xticks=[],yticks=[])
plt.imshow(l2, cmap=plt.cm.flag_r)
plt.subplot(2,5,10,xticks=[],yticks=[])
plt.imshow(l3, cmap=plt.cm.flag_r) | notebooks/Clean Dataset from their spurious pixels.ipynb | jeanpat/DeepFISH | gpl-3.0 | dd4ab010caa4f51ea1821d5d61b5fe23 |
Clean the whole dataset | new_data = np.zeros((1,94,93,2), dtype = int)
N = pairs.shape[0]#10
for idx in range(N):
g_truth = pairs[idx,:,:,1]
grey = pairs[idx,:,:,0]
_, _, _, seg = clean_ground_truth(g_truth, size = 1)
paired = np.dstack((grey, seg))
#
#https://stackoverflow.com/questions/7372316/how-to-make-a-2d-numpy-array-a-3d-array/7372678
#
new_data = np.concatenate((new_data, paired[newaxis,:, :, :]))
new_data = new_data[1:,:,:,:]
plt.figure(figsize=(20,10))
N=10580
grey = new_data[N,:,:,0]
g_truth = new_data[N,:,:,1]
plt.subplot(121,xticks=[],yticks=[])
plt.imshow(grey, cmap=plt.cm.gray)
plt.subplot(122,xticks=[],yticks=[])
plt.imshow(g_truth, cmap=plt.cm.flag_r)
| notebooks/Clean Dataset from their spurious pixels.ipynb | jeanpat/DeepFISH | gpl-3.0 | 9a80de62316c4ebc3a1e526c4a62a208 |
Save the dataset using hdf5 format | filename = './Cleaned_LowRes_13434_overlapping_pairs.h5'
hf = h5py.File(filename,'w')
hf.create_dataset('13434_overlapping_chrom_pairs_LowRes', data=new_data, compression='gzip', compression_opts=9)
hf.close() | notebooks/Clean Dataset from their spurious pixels.ipynb | jeanpat/DeepFISH | gpl-3.0 | d2bfcc83caad61ad592b38361815ca74 |
K-means Clustering non-distributed implementation | X, y_true = make_blobs(n_samples=300, centers=4,
cluster_std=0.60, random_state=0)
# Save simulated data to be used in MapReduce code
np.savetxt("kmeans_simulated_data.txt", X, fmt='%.18e', delimiter=' ')
plt.scatter(X[:, 0], X[:, 1], s=50);
# Write modules for the simulation of local K-Meanss
def assign_clusters(X, m):
clusters = {}
labels = []
for x in X:
#Calculate pair wise distance from each centroid
pair_dist = [(i[0], np.linalg.norm(x-m[i[0]])) for i in enumerate(m)]
#Sort and select the minimum distance centroid
best_centroid = min(pair_dist, key=lambda t:t[1])[0]
labels.append(best_centroid)
try:
clusters[best_centroid].append(x)
except KeyError:
clusters[best_centroid] = [x]
return(clusters, labels)
def evaluate_cluster_mean(clusters):
new_centroid = []
keys = sorted(clusters.keys())
for k in keys:
#Calculate new centroid
new_centroid.append(np.mean(clusters[k], axis = 0))
return(new_centroid)
def check_convergence(new_centroid, old_centroid):
#Check if new and old centroid have changed or not
error = np.all(np.array(new_centroid) == np.array(old_centroid))
return(error)
def driver_kmeans(X, K):
# Initialize Random K centres
old_centroid = random.sample(list(X), K)
new_centroid = random.sample(list(X), K)
#Saving centroid co-ordinates for the comparison with MapReduce code
np.savetxt("kmeans_cache.txt", new_centroid, fmt='%.18e', delimiter=' ')
counter = 0
while not check_convergence(new_centroid, old_centroid):
old_centroid = new_centroid
#Map points to nearest centroid
clusters, labels = assign_clusters(X, new_centroid)
# Find new centroids
new_centroid = evaluate_cluster_mean(clusters)
counter += 1
return(new_centroid, clusters, labels, counter)
#Driver code intialize the mapreduce code
#Not used in the current implementation, added for completion
def init_kmeans(X, K):
centroid = random.sample(list(X), K)
init_centroid = np.array([np.concatenate(([i[0]], i[1])) for i in enumerate(centroid)])
np.savetxt("kmeans_cache.txt", init_centroid, fmt='%.18e', delimiter=' ')
centers, d, labels, counter = driver_kmeans(X, 4)
plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')
cx = [i[0] for i in centers]
cy = [i[1] for i in centers]
plt.scatter(cx, cy, c='black', s=200, alpha=0.5); | codes/driver_kmeans.ipynb | r2rahul/numericalanalysis | gpl-2.0 | ef6ed96fd9405ac01f7206fca8c09829 |
Simulating MapReduce K-Means Algorithm
Mapper Script
Assumes mapper data input in tidy format and all varibales are properly encoded | %%writefile mapper_kmeans.py
import sys
import csv
import math
import numpy as np
#Read the centroids iteratively and its co-ordinates
with open('kmeans_cache.txt', 'r') as f:
fp = csv.reader(f, delimiter = " ")
m = np.array([[float(i) for i in j] for j in fp])
# input comes from STDIN (standard input)
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip().split()
features = np.array([float(j) for j in line])
# Calculate the pair wise distance
pair_dist = [(i[0], np.linalg.norm(features - m[i[0]])) for i in enumerate(m)]
#Sort and select the minimum distance centroid
best_centroid = min(pair_dist, key=lambda t:t[1])[0]
#emit cluster id and coressponding values
out_features = ",".join([str(k) for k in features])
print('{}\t{}'.format(best_centroid, out_features)) | codes/driver_kmeans.ipynb | r2rahul/numericalanalysis | gpl-2.0 | 32ca7c68dec2c6af721a257fd00e7656 |
Reducer Script | %%writefile reducer_kmeans.py
from operator import itemgetter
import sys
import numpy as np
current_cluster = None
current_val = 0
# input comes from STDIN
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
cluster, value = line.split('\t', 1)
#Convert value to float
try:
value = [float(i) for i in value.split(',')]
except ValueError:
#Accounts for error in value inputs. Skips the error lines
continue
#Cluster id as key and corresponding value is passed here
if current_cluster == cluster:
current_val = np.vstack((current_val, value))
else:
if current_cluster:
#Updates the centroids
center = [str(i) for i in np.mean(current_val, axis = 0)]
print('{}'.format(" ".join(center)))
current_val = value
current_cluster= cluster
# To print the last line/clutster id
if current_cluster == cluster:
#Updates the centroids
center = [str(i) for i in np.mean(current_val, axis = 0)]
print('{}'.format(" ".join(center))) | codes/driver_kmeans.ipynb | r2rahul/numericalanalysis | gpl-2.0 | c260d86a11d82183b28d935eaeb69c94 |
Simulate Job Chaining with Shell Script
for loop iterates over each reducer output .
Inside for loop Centroid co-ordinates are updated in kmeans_cache.txt at each iteration .
Final output is stored in kmeans_cache.txt | %%sh
#Initialize the initial clusters
for i in `seq 1 20`;
do
echo 'Iteration Number = '$i
cat kmeans_simulated_data.txt | python mapper_kmeans.py | sort | python reducer_kmeans.py > kmeans_temp.txt
mv kmeans_temp.txt kmeans_cache.txt
done | codes/driver_kmeans.ipynb | r2rahul/numericalanalysis | gpl-2.0 | 2754cd1749c06f464173cbd408729bf4 |
Test MapReduce Implementation | #Check if the centroid calculated in the non-distributed and distributed method are in same range
def check_mapreduce(centroid_non, centroid_dist):
#Check if new and old centroid have changed or not
error = np.all(np.array(centroid_non) == np.array(centroid_dist))
#error calculation second way: Relative Error
num_error = np.linalg.norm(np.array(centroid_non) - np.array(centroid_dist))
return(error, num_error)
#Read the final centroid file
with open('kmeans_cache.txt', 'r') as f:
fp = csv.reader(f, delimiter = " ")
centroid_map = np.array([[float(i) for i in j] for j in fp])
flag, relative_error = check_mapreduce(centers, centroid_map)
if flag:
print("Test Succeded: Both MapReduce and local algorithm returns the same centroids")
elif relative_error < 1e-6:
msg = "Test Succeded: Both MapReduce and local algorithm returns the same centroids with tolerance = "
print('{}\t{}'.format(msg, relative_error))
else:
errmsg = '''Check MapReduce code, perhaps check if both
Mapreduce and Local are initalized from same centroids
Rerun both the codes multiple time to verify'''
print(errmsg) | codes/driver_kmeans.ipynb | r2rahul/numericalanalysis | gpl-2.0 | b96b5191c180c3efe9d84ca9df2455ea |
Value in relationship
If we assume that a relationsip holds some sort of value, how is that value divided?
Think about it...what kinds of value could a relationship hold?
If we think about power in terms of an imbalance in social exchange, how is the value of a relationship distributed based on the power of the individuals of the network?
Where does power come from?
Network Exchange Theory addresses questions of social imbalance and its relation to network structure.
<img style="float:left; width: 400px" src="img/Nelson_and_bart.gif" />
Principles of power | g.add_edges_from([("B", "C"), ("B", "D"), ("D", "E")])
nx.draw_networkx(g) | class19.ipynb | davebshow/DH3501 | mit | f47052988ed5e850b3fb493b6631863b |
Dependence - if relationships confer value, nodes A and C are completely dependent on node B for value.
Exclusion - node B can easily exclude node A or C from the value conferred by the network.
Satiation - at a certain point, nodes like B begin to see diminishing returns and only maintains relations from which they can receive an unequal share of the value.
Betweenness - can confer power, this sort of centrality allows nodes like B to take advantages of structural holes and also control the flow of information througout the network. Note: high betweenness does not always confer an advantage in bargaining situations (as we will soon see).
Experimental methodology: Riddle me this...
<img style="float:left; width: 300px" src="img/experiment_comic.jpg" />
Recall the experimental methodology typically used to study power and exchange? Get together with your pods and refresh your memories...there are five steps.
Are the results of these experiments considered to be robust?
Why or why not (according to E & K)?
Application: The following visualizations show 4 commonly tested paths. What were the experimental results for each path? | g = nx.Graph([("A", "B")])
nx.draw_networkx(g)
plt.title("2-Node Path")
g = nx.Graph([("A", "B"), ("B", "C")])
nx.draw_networkx(g)
plt.title("3-Node Path")
g = nx.Graph([("A", "B"), ("B", "C"), ("C", "D")])
nx.draw_networkx(g)
plt.title("4-Node Path")
g = nx.Graph([("A", "B"), ("B", "C"), ("C", "D"), ("D", "E")])
nx.draw_networkx(g)
plt.title("5-Node Path") | class19.ipynb | davebshow/DH3501 | mit | 704082b03d9563aba21c508304213f32 |
How about power in a network that looks like this? | g = nx.Graph([("A", "B"), ("B", "C"), ("B", "D"), ("C", "D")])
nx.draw_networkx(g)
plt.title("Triangle with outlier") | class19.ipynb | davebshow/DH3501 | mit | d3aa52db99214146eb2f97c3df9964f8 |
Or this? | g = nx.Graph([("A", "B"), ("B", "C"), ("C", "A")])
nx.draw_networkx(g)
plt.title("Triangle") | class19.ipynb | davebshow/DH3501 | mit | 2b4471aa0bf80061f8bd1f6f2df45ec2 |
Locations | HOME_DIR = os.path.expanduser('~').replace('\\', '/')
BASE_DIR = '{}/Documents/DANS/projects/has/dacs'.format(HOME_DIR)
FM_DIR = '{}/fm'.format(BASE_DIR)
FMNS = '{http://www.filemaker.com/fmpxmlresult}'
CONFIG_DIR = '.' | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 170afb99a4e91d9d2e418ea45f00b00f |
Config
All configuration in a big yaml file | with open('{}/config.yaml') | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 87f922727673c534517e0d1fe6c3c006 |
Data description
Main source tables and fields to skip | CONFIG = yaml.load('''
mainTables:
- contrib
- country
''')
mainTables = ('contrib', 'country')
SKIP_FIELDS = dict(
contrib=set('''
dateandtime_ciozero
ikid
ikid_base
find_country_id
find_type
gnewpassword
gnewpassword2
goldpassword
help_description
help_text
message
message_allert
teller
total_costs_total
whois
'''.strip().split()),
country=set('''
'''.strip().split()),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 361529016221f1748bdba04928ac21f3 |
Fields to merge | MERGE_FIELDS = dict(
contrib=dict(
academic_entity_url=['academic_entity_url_2'],
contribution_url=['contribution_url_2'],
contact_person_mail=['contact_person_mail_2'],
type_of_inkind=['other_type_of_inkind'],
vcc11_name=[
'vcc12_name',
'vcc21_name',
'vcc22_name',
'vcc31_name',
'vcc32_name',
'vcc41_name',
'vcc42_name',
],
vcc_head_decision_vcc11=[
'vcc_head_decision_vcc12',
'vcc_head_decision_vcc21',
'vcc_head_decision_vcc22',
'vcc_head_decision_vcc31',
'vcc_head_decision_vcc32',
'vcc_head_decision_vcc41',
'vcc_head_decision_vcc42',
],
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | ddebb406bd00ea3d1fdfca31cb397e90 |
Fields to rename | MAP_FIELDS = dict(
contrib=dict(
approved='approved',
academic_entity_url='urlAcademic',
contribution_url='urlContribution',
contact_person_mail='contactPersonEmail',
contact_person_name='contactPersonName',
costs_description='costDescription',
costs_total='costTotal',
country='country',
creation_date_time='dateCreated',
creator='creator',
dateandtime_approval='dateApproved',
dateandtime_cioapproval='dateApprovedCIO',
description_of_contribution='description',
disciplines_associated='discipline',
last_modifier='modifiedBy',
modification_date_time='dateModified',
other_keywords='keyword',
submit='submitted',
tadirah_research_activities='tadirahActivity',
tadirah_research_objects='tadirahObject',
tadirah_research_techniques='tadirahTechnique',
title='title',
total_costs_total='costTotalTotal',
type_of_inkind='typeContribution',
vcc='vcc',
vcc11_name='reviewerName',
vcc_head_decision='vccDecision',
vcc_head_decision_vcc11='reviewerDecision',
vcchead_approval='vccApproval',
vcchead_disapproval='vccDisApproval',
year='year',
),
country=dict(
countrycode='iso',
countryname='name',
member_dariah='isMember',
),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 72c68b1109e6918e7fafa3b2345aad87 |
Fields to split into multiple values | generic = re.compile('[ \t]*[\n+][ \t\n]*') # split on newlines (with surrounding white space)
genericComma = re.compile('[ \t]*[\n+,;][ \t\n]*') # split on newlines or commas (with surrounding white space)
SPLIT_FIELDS=dict(
contrib=dict(
discipline=generic,
keyword=genericComma,
typeContribution=generic,
tadirahActivity=generic,
tadirahObject=generic,
tadirahTechnique=generic,
vcc=generic,
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | ac4aaac6f29689561dde61bee806b9c2 |
Fields to hack | STRIP_NUM = re.compile('^[0-9]\s*\.?\s+')
def stripNum(v): return STRIP_NUM.sub('', v)
HACK_FIELDS=dict(
contrib=dict(
tadirahActivity=stripNum,
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 454d8abdf9a672e7974a40f901973e19 |
Fields to decompose into several fields | DECOMPOSE_FIELDS=dict(
contrib=dict(
typeContribution='typeContributionOther',
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | f01d271638585e2df44c8f3548ffd454 |
Custom field types | FIELD_TYPE = dict(
contrib=dict(
costTotal='valuta',
dateCreated='datetime',
dateModified='datetime',
dateApproved='datetime',
dateApprovedCIO='datetime',
contactPersonEmail='email',
submitted='bool',
approved='bool',
reviewerDecision='bool',
vccApproval='bool',
vccDecision='bool',
vccDisApproval='bool',
),
country=dict(
isMember='bool',
),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | bb8f494ab9eca28e14c0f36d75a47980 |
Default values | DEFAULT_VALUES=dict(
contrib=dict(
dateCreated=datetime(2000,1,1,0,0,0),
creator="admin",
type_of_inkind="General",
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | f0ce9c10865c8b8d69090d2ab1830962 |
Fields to move to other tables | MOVE_FIELDS=dict(
contrib=dict(
assessment=set('''
approved
dateApproved
dateApprovedCIO
submitted
reviewerName
reviewerDecision
vccDecision
vccApproval
vccDisApproval
'''.strip().split()),
),
country=dict(),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 7cbe65eaf1e5708a86bc4e80d3fca8a3 |
Fields to value lists | MAKE_VALUE_LISTS = dict(
contrib=set('''
keyword
year
'''.strip().split()),
)
VALUE_LISTS = dict(
contrib=set('''
discipline
keyword
tadirahActivity
tadirahObject
tadirahTechnique
typeContribution
typeContributionOther:typeContribution
vcc
year
'''.strip().split()),
)
MOVE_MISSING = dict(
contrib='description',
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 5cf8ace9ce6fb75ab69c3b1dfc4a6364 |
Field values
Patterns for value types | # Source field types, including types assigned by type overriding (see FIELD_TYPE_OVERRIDE above).
# These will be translated into appropriate SQL field types
TYPES = {'bool', 'number', 'decimal', 'text', 'valuta', 'email', 'date', 'datetime'}
# dates are already in ISO (date2_pattern).
# If we encounter other dates, we could use date_pattern instead)
# datetimes are not in iso, they will be transformed to iso.
DECIMAL_PATTERN = re.compile(
r'^-?[0-9]+\.?[0-9]*'
)
DATE_PATTERN = re.compile(
r'^\s*([0-9]{2})/([0-9]{2})/([0-9]{4})$'
)
DATE2_PATTERN = re.compile(
r'^\s*([0-9]{4})-([0-9]{2})-([0-9]{2})$'
)
DATETIME_PATTERN = re.compile(
r'^\s*([0-9]{2})/([0-9]{2})/([0-9]{4})\s+([0-9]{2}):([0-9]{2})(?::([0-9]{2}))?$'
)
# meaningless values will be translated into None
NULL_VALUES = {
'http://',
'https://',
'@',
}
BOOL_VALUES = {
True: {'Yes', 'YES', 'yes', 1, '1', True},
False: {'No', 'NO', 'no', 0, '0', 'NULL', False},
} | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 83381ca3fcc574fbdaf1d2779c3c5f2a |
Date and Time values | def date_repl(match):
[d,m,y] = list(match.groups())
return '{}-{}-{}'.format(y,m,d)
def date2_repl(match):
[y,m,d] = list(match.groups())
return '{}-{}-{}'.format(y,m,d)
def datetime_repl(match):
[d,m,y,hr,mn,sc] = list(match.groups())
return '{}-{}-{}T{}:{}:{}'.format(y,m,d,hr,mn,sc or '00')
def dt(v_raw, i, t, fname):
if not DATE2_PATTERN.match(v_raw):
warning(
'table `{}` field `{}` record {}: not a valid date: "{}"'.format(
t, fname, i, v_raw
))
return v_raw
return datetime(*map(int, re.split('[:T-]', DATE2_PATTERN.sub(date2_repl, v_raw))))
def dtm(v_raw, i, t, fname):
if not DATETIME_PATTERN.match(v_raw):
warning(
'table `{}` field `{}` record {}: not a valid date time: "{}"'.format(
t, fname, i, v_raw
))
return v_raw
return datetime(*map(int, re.split('[:T-]', DATETIME_PATTERN.sub(datetime_repl, v_raw)))) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 7d1257571585efe3b8c38fadaa8b795f |
Boolean, numeric and string values | def bools(v_raw, i, t, fname):
if v_raw in BOOL_VALUES[True]: return True
if v_raw in BOOL_VALUES[False]: return False
warning(
'table `{}` field `{}` record {}: not a boolean value: "{}"'.format(
t, fname, i, v_raw
))
return v_raw
def num(v_raw, i, t, fname):
if type(v_raw) is int: return v_raw
if v_raw.isdigit(): return int(v_raw)
warning(
'table `{}` field `{}` record {}: not an integer: "{}"'.format(
t, fname, i, v_raw
))
return v_raw
def decimal(v_raw, i, t, fname):
if type(v_raw) is float: return v_raw
if v_raw.isdigit(): return float(v_raw)
if DECIMAL_PATTERN.match(v_raw): return float(v_raw)
warning(
'table `{}` field `{}` record {}: not an integer: "{}"'.format(
t, fname, i, v_raw
))
return v_raw
def email(v_raw, i, t, fname):
return v_raw.replace('mailto:', '', 1) if v_raw.startswith('mailto:') else v_raw | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | c878423cd3f2ccbd24802dd76d401d83 |
Money values | def money(v_raw, i, t, fname):
note = ',' in v_raw or '.' in v_raw
v = v_raw.strip().lower().replace(' ','').replace('€', '').replace('euro', '').replace('\u00a0', '')
for p in range(2,4): # interpret . or , as decimal point if less than 3 digits follow it
if len(v) >= p and v[-p] in '.,':
v_i = v[::-1]
if v_i[p-1] == ',': v_i = v_i.replace(',', 'D', 1)
elif v_i[p-1] == '.': v_i = v_i.replace('.', 'D', 1)
v = v_i[::-1]
v = v.replace('.','').replace(',','')
v = v.replace('D', '.')
if not v.replace('.','').isdigit():
if len(set(v) & set('0123456789')):
warning(
'table `{}` field `{}` record {}: not a decimal number: "{}" <= "{}"'.format(
t, fname, i, v, v_raw,
))
money_warnings.setdefault('{}:{}'.format(t, fname), {}).setdefault(v, set()).add(v_raw)
v = None
else:
v = None
money_notes.setdefault('{}:{}'.format(t, fname), {}).setdefault('NULL', set()).add(v_raw)
elif note:
money_notes.setdefault('{}:{}'.format(t, fname), {}).setdefault(v, set()).add(v_raw)
return None if v == None else float(v) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | d838966f42786a2b6e1b4d07e97be711 |
Clean up field values | def sanitize(t, i, fname, value):
if fname == '_id': return value
(ftype, fmult) = allFields[t][fname]
newValue = []
for v_raw in value:
if v_raw == None or v_raw in NULL_VALUES: continue
elif ftype == 'text': v = v_raw
elif ftype == 'bool': v = bools(v_raw, i, t, fname)
elif ftype == 'number': v = num(v_raw, i, t, fname)
elif ftype == 'decimal': v = decimal(v_raw, i, t, fname)
elif ftype == 'email': v = email(v_raw, i, t, fname)
elif ftype == 'valuta': v = money(v_raw, i, t, fname)
elif ftype == 'date': v = dt(v_raw, i, t, fname)
elif ftype == 'datetime': v = dtm(v_raw, i, t, fname)
else: v = v_raw
if v != None and (fmult <= 1 or v != ''): newValue.append(v)
if len(newValue) == 0:
defValue = DEFAULT_VALUES.get(t, {}).get(fname, None)
if defValue != None:
newValue = [defValue]
return newValue | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | e7eacc4b6cfa6c7dc5170652562f0013 |
Show information | def info(x): sys.stdout.write('{}\n'.format(x))
def warning(x): sys.stderr.write('{}\n'.format(x))
def showFields():
for (mt, defs) in sorted(allFields.items()):
info(mt)
for (fname, fdef) in sorted(defs.items()):
info('{:>25}: {:<10} ({})'.format(fname, *fdef))
def showdata(rows):
for row in rows:
for f in sorted(row.items()):
info('{:>20} = {}'.format(*f))
info('o-o-o-o-o-o-o-o-o-o-o-o')
def showData():
for (mt, rows) in sorted(allData.items()):
info('o-o-o-o-o-o-o TABLE {} with {} rows o-o-o-o-o-o-o-o '.format(mt, len(rows)))
showdata(rows[0:2])
def showMoney():
for tf in sorted(money_notes):
for v in sorted(money_notes[tf]):
info('{} "{}" <= {}'.format(
tf, v,
' | '.join(money_notes[tf][v]),
)) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | deaf197c71858630e86c28b56d4aa366 |
Read FM fields | def readFmFields():
for mt in mainTables:
infile = '{}/{}.xml'.format(FM_DIR, mt)
root = etree.parse(infile, parser).getroot()
fieldroots = [x for x in root.iter(FMNS+'METADATA')]
fieldroot = fieldroots[0]
fields = []
fieldDefs = {}
for x in fieldroot.iter(FMNS+'FIELD'):
fname = x.get('NAME').lower().replace(' ','_').replace(':', '_')
ftype = x.get('TYPE').lower()
fmult = int(x.get('MAXREPEAT'))
fields.append(fname)
fieldDefs[fname] = [ftype, fmult]
rawFields[mt] = fields
allFields[mt] = fieldDefs
for f in SKIP_FIELDS[mt]:
del allFields[mt][f]
for (f, mfs) in MERGE_FIELDS[mt].items():
allFields[mt][f][1] += 1
for mf in mfs:
del allFields[mt][mf]
allFields[mt] = dict((MAP_FIELDS[mt][f], v) for (f,v) in allFields[mt].items())
for f in SPLIT_FIELDS[mt]:
allFields[mt][f][1] += 1
for (f, fo) in DECOMPOSE_FIELDS[mt].items():
allFields[mt][fo] = allFields[mt][f]
allFields[mt][f] = [allFields[mt][f][0], 1]
for (f, t) in FIELD_TYPE[mt].items():
allFields[mt][f][0] = t | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 9ec8ec8992c9e8f1ddc44094545c1d31 |
Read FM data | def readFmData():
for mt in mainTables:
infile = '{}/{}.xml'.format(FM_DIR, mt)
root = etree.parse(infile, parser).getroot()
dataroots = [x for x in root.iter(FMNS+'RESULTSET')]
dataroot = dataroots[0]
rows = []
rowsRaw = []
fields = rawFields[mt]
for (i, r) in enumerate(dataroot.iter(FMNS+'ROW')):
rowRaw = []
for c in r.iter(FMNS+'COL'):
data = [x.text.strip() for x in c.iter(FMNS+'DATA') if x.text != None]
rowRaw.append(data)
if len(rowRaw) != len(fields):
warning('row {}: fields encountered = {}, should be {}'.format(len(row), len(fields)))
rowsRaw.append(dict((f,v) for (f, v) in zip(fields, rowRaw)))
row = dict((f,v) for (f, v) in zip(fields, rowRaw) if f not in SKIP_FIELDS[mt])
for (f, mfs) in MERGE_FIELDS[mt].items():
for mf in mfs:
row[f].extend(row[mf])
del row[mf]
row = dict((MAP_FIELDS[mt][f], v) for (f,v) in row.items())
for (f, spl) in SPLIT_FIELDS[mt].items():
row[f] = reduce(lambda x,y: x+y, [spl.split(v) for v in row[f]], [])
for (f, hack) in HACK_FIELDS[mt].items():
row[f] = [hack(v) for v in row[f]]
for (f, fo) in DECOMPOSE_FIELDS[mt].items():
row[fo] = row[f][1:]
row[f] = [row[f][0]] if len(row[f]) else []
row['_id'] = ObjectId()
#info('\n'.join('{}={}'.format(*x) for x in sorted(row.items())))
for (f, v) in row.items(): row[f] = sanitize(mt, i, f, v)
rows.append(row)
allData[mt] = rows
rawData[mt] = rowsRaw
if money_warnings:
for tf in sorted(money_warnings):
for v in sorted(money_warnings[tf]):
warning('{} "{}" <= {}'.format(
tf, v,
' | '.join(money_warnings[tf][v]),
)) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | f703f2c95a124736fcfafa8bac1f218b |
Split tables into several tables by column groups | def moveFields():
for mt in mainTables:
for (omt, mfs) in MOVE_FIELDS[mt].items():
for mf in mfs:
allFields.setdefault(omt, dict())[mf] = allFields[mt][mf]
del allFields[mt][mf]
allFields.setdefault(omt, dict)['{}_id'.format(mt)] = ('id', 1)
for row in allData[mt]:
for (omt, mfs) in MOVE_FIELDS[mt].items():
orow = dict((mf, row[mf]) for mf in mfs)
orow['_id'] = ObjectId()
orow['{}_id'.format(mt)] = row['_id']
allData.setdefault(omt, []).append(orow)
for mf in mfs: del row[mf] | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | bd88a8b42a30e56e9c423f276bf13bd0 |
Value Lists | def readLists():
valueLists = dict()
for path in glob('{}/*.txt'.format(FM_DIR)):
tname = basename(splitext(path)[0])
data = []
with open(path) as fh:
for line in fh:
data.append(line.rstrip().split('\t'))
valueLists[tname] = data
for (vList, data) in valueLists.items():
if vList == 'countryExtra':
mapping = dict((x[0], x[1:]) for x in data)
else:
mapping = dict((i+1, x[0]) for (i, x) in enumerate(data))
valueDict[vList] = mapping
allFields[vList] = dict(
_id=('id', 1),
value=('string', 1),
)
for mt in allData:
fs = MAKE_VALUE_LISTS.get(mt, set())
for f in fs:
valSet = set()
for row in allData[mt]:
values = row.get(f, [])
if type(values) is not list:
values = [values]
valSet |= set(values)
valueDict[f] = dict((i+1, x) for (i, x) in enumerate(sorted(valSet)))
allFields[f] = dict(
_id=('id', 1),
value=('string', 1),
) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | c5eda7aea31f6d844d9982b4e79ae50b |
Country table | def countryTable():
extraInfo = valueDict['countryExtra']
idMapping = dict()
for row in allData['country']:
for f in row:
if type(row[f]) is list: row[f] = row[f][0]
iso = row['iso']
row['_id'] = ObjectId()
idMapping[iso] = row['_id']
(name, lat, long) = extraInfo[iso]
row['latitude'] = lat
row['longitude'] = long
for row in allData['contrib']:
newValue = []
for iso in row['country']:
newValue.append(dict(_id=idMapping[iso], iso=iso, value=extraInfo[iso][0]))
row['country'] = newValue
allFields['country']['_id'] = ('id', 1)
allFields['country']['iso'] = ('string', 1)
allFields['country']['latitude'] = ('float', 1)
allFields['country']['longitude'] = ('float', 1)
| static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | fc38350ba378d81f8700e538bfc9e8ab |
User table | def userTable():
idMapping = dict()
existingUsers = []
testUsers = [
dict(eppn='suzan', email='suzan1@test.eu', mayLogin=True, authority='local',
firstName='Suzan', lastName='Karelse'),
dict(eppn='marie', email='suzan2@test.eu', mayLogin=True, authority='local',
firstName='Marie', lastName='Pieterse'),
dict(eppn='gertjan', email='gertjan@test.eu', mayLogin=False, authority='local',
firstName='Gert Jan', lastName='Klein-Holgerink'),
dict(eppn='lisa', email='lisa@test.eu', mayLogin=True, authority='local',
firstName='Lisa', lastName='de Leeuw'),
dict(eppn='dirk', email='dirk@test.eu', mayLogin=True, authority='local',
firstName='Dirk', lastName='Roorda'),
]
users = collections.defaultdict(set)
eppnSet = set()
for c in allData['contrib']:
crs = c.get('creator', []) + c.get('modifiedBy', [])
for cr in crs:
eppnSet.add(cr)
idMapping = dict((eppn, ObjectId()) for eppn in sorted(eppnSet))
for c in allData['contrib']:
c['creator'] = [dict(_id=idMapping[cr]) for cr in c['creator']]
if 'modifiedBy' not in c:
c['modifiedBy'] = []
else:
c['modifiedBy'] = [dict(_id=idMapping[cr]) for cr in c['modifiedBy']]
users = dict((i, eppn) for (eppn, i) in idMapping.items())
for (i, eppn) in sorted(users.items()):
existingUsers.append(dict(_id=i, eppn=eppn, mayLogin=False, authority='legacy'))
for u in testUsers:
u['_id'] = ObjectId()
idMapping[u['eppn']] = u['_id']
existingUsers.append(u)
inGroups = [
dict(eppn='DirkRoorda@dariah.eu', authority='DARIAH', group='system'),
dict(eppn='LisaDeLeeuw@dariah.eu', authority='DARIAH', group='office'),
dict(eppn='suzan', authority='local', group='auth'),
dict(eppn='marie', authority='local', group='auth'),
dict(eppn='gertjan', authority='local', group='auth'),
dict(eppn='lisa', authority='local', group='office'),
dict(eppn='dirk', authority='local', group='system'),
]
inGroups = [dict(tuple(ig.items())+(('_id', ObjectId()),)) for ig in inGroups]
allData['user'] = existingUsers
allData['group'] = inGroups
allFields['user'] = dict(
_id=('id', 1),
eppn=('string', 1),
email=('email', 1),
mayLogin=('bool', 1),
authority=('string', 1),
firstName=('string', 1),
lastName=('string', 1),
)
allFields['group'] = dict(
_id=('id', 1),
eppn=('string', 1),
authority=('string', 1),
group=('string', 1),
)
uidMapping.update(idMapping) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 1deeb048623e57d844bcad324c79b53f |
Related tables | def relTables():
def norm(x): return x.strip().lower()
relIndex = dict()
for mt in sorted(VALUE_LISTS):
rows = allData[mt]
for f in sorted(VALUE_LISTS[mt]):
comps = f.split(':')
if len(comps) == 2:
(f, fAs) = comps
else:
fAs = f
relInfo = valueDict[fAs]
if not fAs in relIndex:
idMapping = dict((i, ObjectId()) for i in relInfo)
allData[fAs] = [dict(_id=idMapping[i], value=v) for (i, v) in relInfo.items()]
relIndex[fAs] = dict((norm(v), (idMapping[i], v)) for (i, v) in relInfo.items())
for row in rows:
newValue = []
for v in row[f]:
rnv = norm(v)
(i, nv) = relIndex[fAs].get(rnv, ("-1", None))
if nv == None:
target = MOVE_MISSING[mt]
if target not in row: row[target] = ['']
row[target][0] += '\nMOVED FROM {}: {}'.format(f, v)
else: newValue.append(dict(_id=i, value=nv))
row[f] = newValue | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 3d661698622acdadd6c8442cdad692aa |
Test tweaks
Tweaks for testing purposes. | def testTweaks():
mt = 'contrib'
myContribs = {'3DHOP', 'AAI'}
my = uidMapping['dirk']
for row in allData[mt]:
title = row.get('title', [None])
if len(title) == 0: title = [None]
if title[0] in myContribs:
row['creator'] = [dict(_id=my)] | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 22a0dda7061c11ff888e0710150eaecd |
Insert into MongoDB | def importMongo():
client = MongoClient()
client.drop_database('dariah')
db = client.dariah
for (mt, rows) in allData.items():
info(mt)
db[mt].insert_many(rows) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 621393b433f40b7c442688a2b6482299 |
The whole pipeline | money_warnings = {}
money_notes = {}
valueDict = dict()
rawFields = dict()
allFields = dict()
rawData = dict()
allData = dict()
uidMapping = dict()
parser = etree.XMLParser(remove_blank_text=True, ns_clean=True)
readFmFields()
readFmData()
readLists()
moveFields()
countryTable()
userTable()
relTables()
testTweaks()
importMongo()
#showData()
#showMoney() | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 2b5ad80b7ee3dc5291a28af7fb08db5f |
To import the bson dump in another mongodb installation, use the commandline to dump the dariah database here
mongodump -d dariah -o dariah
and to import it elsewhere.
mongorestore --drop -d dariah dariah | valueDict.keys()
valueDict['keywords'] | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | c0d29bcdaa45f9ab3c2fee79f61991c0 |
Exploration
The process has finished, but here is space to explore the data, in order to find patterns, regularities, and, more importantly, irregularities.
First step: create csv files of the data and combine them into an excel sheet. | import xlsxwriter
EXPORT_DIR = os.path.expanduser('~/Downloads')
EXPORT_ORIG = '{}/contribFromFileMaker.xlsx'.format(EXPORT_DIR)
EXPORT_MONGO = '{}/contribInMongoDB.xlsx'.format(EXPORT_DIR)
workbook = xlsxwriter.Workbook(EXPORT_ORIG, {'strings_to_urls': False})
for mt in rawData:
worksheet = workbook.add_worksheet(mt)
for (f, field) in enumerate(rawFields[mt]):
worksheet.write(0, f, field)
for (r, row) in enumerate(rawData[mt]):
for (f, field) in enumerate(rawFields[mt]):
val = row[field]
val = [] if val == None else val if type(val) is list else [val]
val = '|'.join(val)
worksheet.write(r+1, f, val)
workbook.close()
workbook = xlsxwriter.Workbook(EXPORT_MONGO, {'strings_to_urls': False})
for mt in allData:
worksheet = workbook.add_worksheet(mt)
fields = sorted(allFields[mt])
for (f, field) in enumerate(fields):
worksheet.write(0, f, field)
for (r, row) in enumerate(allData[mt]):
for (f, field) in enumerate(fields):
fmt = None
val = row.get(field, [])
(ftype, fmult) = allFields[mt][field]
val = [] if val == None else [val] if type(val) is not list else val
exportVal = []
for v in val:
if type(v) is dict:
exportVal.append(','.join(str(vv) for vv in v.values()))
elif ftype == 'date' or ftype == 'datetime':
exportVal.append(v if type(v) is str else v.isoformat())
else:
exportVal.append(str(v))
worksheet.write(r+1, f, ' | '.join(exportVal))
workbook.close()
showFields()
client = MongoClient()
dbm = client.dariah
for d in dbm.contrib.find({'title': '3DHOP'}).limit(2):
print('=' * 50)
for f in sorted(d):
print('{}={}'.format(f, d[f])) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 395f577037a8191b3fd18fcf550d8259 |
Here is a query to get all 'type_of_inkind' values for contributions. | for c in dbm.contrib.distinct('typeContribution', {}):
print(c) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | d9fe22a609a0873449ccd2f68e0723c8 |
Here are the users: | for c in dbm.users.find({}):
print(c) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 93da8e4f368bcdc03efbd4406cd296e0 |
Here are the countries: | for c in dbm.country.find({'isMember': True}):
print(c)
for c in dbm.contrib.distinct('country', {}):
print(c) | static/tools/.ipynb_checkpoints/from_filemaker-checkpoint.ipynb | Dans-labs/dariah | mit | 208df5c45278228c784cded57b32dc81 |