In [1]:
# Necessary import evil
%matplotlib inline
from physt import histogram, binnings, h1, h2, h3
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1337) # Have always the same data
I believe you can skip this section but anyway, for the sake of completeness, the default way of installing a relatively stable version of physt is via pip:
pip install physt
Alternatively, you can download the source code from github (https://github.com/janpipek/physt).
You will need numpy to use physt (required), but there are other packages (optional) that are very useful if you want to use physt at its best: matplotlib for plotting (or bokeh as a not-so-well supported alternative).
In [2]:
# Basic dataset
heights = [160, 155, 156, 198, 177, 168, 191, 183, 184, 179, 178, 172, 173, 175,
172, 177, 176, 175, 174, 173, 174, 175, 177, 169, 168, 164, 175, 188,
178, 174, 173, 181, 185, 166, 162, 163, 171, 165, 180, 189, 166, 163,
172, 173, 174, 183, 184, 161, 162, 168, 169, 174, 176, 170, 169, 165]
hist = histogram(heights) # Automatically select all settings
hist
Out[2]:
...which is an object of the Histogram1D type that holds all the bin information...
In [3]:
hist.bins # All the bins
Out[3]:
In [4]:
hist.frequencies # All the frequencies
Out[4]:
...and provides further features and methods, like plotting for example...
In [5]:
hist.plot(show_values=True);
...or adding new values (note that this is something numpy.histogram won't do for you)...
In [6]:
original = hist.copy() # Store the original data to see changes
# ******* Here comes a lonely giant
hist.fill(197)
step1 = hist.copy() # Store the intermediate value
# ******* And a bunch of relatively short people
hist.fill_n([160, 160, 161, 157, 156, 159, 162])
# See how the plot changes (you can safely ignore the following 4 lines)
ax = hist.plot(label="After fill_n");
step1.plot(color="yellow", ax=ax, label="After fill")
original.plot(color="red", ax=ax, label="Before filling")
ax.legend(loc=1)
# See the number of entries
hist
Out[6]:
The primary goal of physt library is to represent histograms as data objects with a set methods for easy manipulation and analysis (including mathematical operations, adaptivity, summary statistics. ...). The histogram classes used in ROOT framework served as inspiration but not as a model to copy (though relevant methods often have same names).
Based on its dimensionality, a histogram is an instance of one of the following classes (all inheriting from HistogramBase):
However, these objects are __init__ialized with already calculated data and therefore, you typically don't construct the yourselves but call one of the facade functions:
These functions try to find the best binning schema, calculate bin contents and set other properties for the histograms. In principle (if not, submit a bug report), if you call a function with arguments understood by eponymous numpy functions (histogram, histogram2d and histogramdd), you should receive histogram with exactly the same bin edges and bin contents. However, there are many more arguments available!
In [7]:
# Back to people's parameters...
heights = np.random.normal(172, 10, 100)
weights = np.random.normal(70, 15, 100)
iqs = np.random.normal(100, 15, 100)
In [8]:
# 1D histogram
h1(heights)
Out[8]:
In [9]:
# 2D histogram
h2(heights, weights, [5, 7])
Out[9]:
In [10]:
# 3D histogram
h3([heights, weights, iqs]) # Simplification with respect to numpy.histogramdd
Out[10]:
So, what do these objects contain? In principle:
_binning or _binnings)frequencies) together with errors (errors)mean, variance, std)name and axis_name or axis_names)In the following, properties of Histogram1D will be described. Analogous methods and data fields do exist also for Histogram2D and HistogramND, perhaps with the name in plural.
The structure of bins is stored in the histogram object as a hidden attribute _binning. This value is an instance of one of the binning classes that are all descendants of physt.binnings.BinningBase. You are not supposed to directly access this value because manipulating it without at the same time updating the bin contents is dangerous.
A dedicated notebook deals with the binning specifics, here we sum at least the most important features.
Histogram1D offers the following attributes to access (read-only or read-only-intended) the binning information (explicitly or implicitly stored in _binning):
In [11]:
# Create a histogram with "reasonable" bins
data = np.random.normal(0, 7, 10000)
hist = histogram(data, "human", 4)
hist
Out[11]:
In [12]:
hist._binning # Just to show, don't use it
Out[12]:
In [13]:
hist.bin_count # The total number of bins
Out[13]:
In [14]:
hist.bins # Bins as array of both edges
Out[14]:
In [15]:
hist.numpy_bins # Bin edges with the same semantics as the numpy.histogram
Out[15]:
In [16]:
hist.bin_left_edges
Out[16]:
In [17]:
hist.bin_right_edges
Out[17]:
In [18]:
hist.bin_centers # Centers of the bins - useful for interpretation of histograms as scatter data
Out[18]:
In [19]:
hist.bin_widths # Widths of the bins - useful for calculating densities and also for bar plots
Out[19]:
Just as a simple overview of binning schemas, that are provided by physt, we show the bins as produced by different schemas:
In [20]:
list(binnings.binning_methods.keys()) # Show them all
Out[20]:
These names can be used as the second parameter of the h1 function:
In [21]:
# Fixed-width
h1(data, "fixed_width", 6).numpy_bins
Out[21]:
In [22]:
# Numpy-like
print("Expected:", np.histogram(data, 5)[1])
print("We got:", h1(data, "numpy", 5).numpy_bins)
In [23]:
# Integer - centered around integers; useful for integer data
h1(data, "integer").numpy_bins
Out[23]:
In [24]:
# Exponential - positive numbers required
h1(np.abs(data), "exponential").numpy_bins # We 'abs' the values
Out[24]:
In [25]:
# Quantile - each bin should have a similar statistical importance
h1(data, "quantile", 5).numpy_bins
Out[25]:
In [26]:
# Human - as friendly to your plots as possible, you may set an approximate number of bins
h1(data, "human").numpy_bins
Out[26]:
The bin contents (frequencies) and associated errors (errors) are stored as numpy arrays with a shape corresponding to number of bins (in all dimensions). Again, you cannot manipulate these properties diractly (unless you break the dont-touch-the-underscore convention).
In [27]:
hist = h1(data, "human")
hist.frequencies
Out[27]:
Errors are calculated as $\sqrt(N)$ which is the simplest expectation for independent values. If you don't accept this, you can set your errors through _errors2 field which contains squared errors.
Note: Filling with weights, arithmetic operations and scaling preserve correct error values under similar conditions.
In [28]:
hist.errors
Out[28]:
In [29]:
# Doubling the histogram doubles the error
(hist * 2).errors
Out[29]:
Internally, histogram bins can contain values in several types (dtype in numpy terminology). By default, this is either np.int64 (for histograms without weights) or np.float64 (for histograms with weight). Wherever possible, this distinction is preserved. If you try filling in with weights, if you multiply by a float constant, if you divide, ... - basically whenever this is reasonable - an integer histogram is automatically converted to a float one.
In [30]:
hist = h1(data)
print("Default type:", hist.dtype)
hist = h1(data, weights=np.abs(data)) # Add some random weights
print("Default type with weights:", hist.dtype)
hist = h1(data)
hist.fill(1.0, weight=.44)
print("Default type after filling with weight:", hist.dtype)
hist = h1(data)
hist *= 2
print("Default type after multiplying by an int:", hist.dtype)
hist *= 5.6
print("Default type after multiplying by a float:", hist.dtype)
In [31]:
# You can specify the type in the method call
hist = h1(data, dtype="int32")
hist.dtype
Out[31]:
In [32]:
# You can set the type of the histogram using the attribute
hist = h1(data)
hist.dtype = np.int32
hist.dtype
Out[32]:
In [33]:
# Adding two histograms uses the broader range
hist1 = h1(data, dtype="int64")
hist2 = h1(data, dtype="float32")
(hist1 + hist2).dtype # See the result!
Out[33]:
As mentioned, h1 and h2 are just facade functions. You can construct the objects directly using the constructors. The first argument accepts something that can be interpreted as binning or list of bins, second argument is an array of frequencies (numpy array or something convertible).
In [34]:
from physt.histogram1d import Histogram1D
from physt.histogram_nd import Histogram2D
hist1 = Histogram1D([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], [1, 2, 3, 4, 5])
hist2 = Histogram2D([[0, 0.5, 1], [0, 1, 2, 3]], [[0.2, 2.2, 7.3], [6, 5, 3]], axis_names=["x", "y"])
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
hist1.plot(ax = axes[0])
hist2.plot(ax = axes[1])
hist1, hist2
Out[34]:
In [35]:
# Create a physt "logo", also available as physt.examples.fist
_, ax = plt.subplots(figsize=(4, 4))
widths = np.cumsum([0, 1.2, 0.2, 1, 0.1, 1, 0.1, 0.9, 0.1, 0.8])
fingers = np.asarray([4, 1, 7.5, 6, 7.6, 6, 7.5, 6, 7.2]) + 5
hist1 = Histogram1D(widths, fingers)
hist1.plot(lw=0, ax=ax)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel("physt")
hist1
Out[35]:
Supported indexing is more or less compatible with numpy arrays.
In [36]:
hist.find_bin(3) # Find a proper bin for some value (0 - based indices)
Out[36]:
In [37]:
hist[3] # Return the bin (with frequency)
Out[37]:
In [38]:
hist[-3:] # Sub-histogram (as slice)
Out[38]:
In [39]:
hist[hist.frequencies > 5] # Masked array (destroys underflow & overflow information)
Out[39]:
In [40]:
hist[[1, 3, 5]] # Select some of the bins
Out[40]:
In [41]:
hist.mean()
Out[41]:
In [42]:
hist.std()
Out[42]:
In [43]:
hist.plot(); # Basic plot
In [44]:
hist.plot(density=True, errors=True, ecolor="red"); # Include errors
In [45]:
hist.plot(show_stats=True, errors=True, alpha=0.3); # Show summary statistics (not fully supported yet)
In [46]:
hist.plot(cumulative=True, color="yellow", lw=3, edgecolor="red"); # Use matplotlib parameters
In [47]:
hist.plot(kind="scatter", s=hist.frequencies, cmap="rainbow", density=True); # Another plot type
In [48]:
hist.plot(kind="step", lw=4)
Out[48]:
In [49]:
# Plot different bins using different styles
axis = hist[hist.frequencies > 5].plot(label="High", alpha=0.5)
hist[1:-1][hist[1:-1].frequencies <= 5].plot(ax=axis, color="green", label="Low", alpha=0.5)
hist[[0, -1]].plot(ax=axis, color="red", label="Edge cases", alpha=0.5)
hist.plot(kind="scatter", ax=axis, s=hist.frequencies / 10, label="Scatter")
# axis.legend(); # Does not work - why?
Out[49]:
In [50]:
# Bar plot with colormap (with logarithmic scale)
ax = hist.plot(cmap="Reds_r", yscale="log", show_values=True);
In [51]:
figure, axes = plt.subplots(1, 3, figsize=(11, 3))
hist_irregular = histogram(heights, [160, 162, 166, 167, 175, 188, 191])
hist_irregular.plot(ax=axes[0], errors=True, cmap="rainbow");
hist_irregular.plot(ax=axes[1], density=True, errors=True, cmap="rainbow");
hist_irregular.plot(ax=axes[2], density=True, cumulative=True, cmap="rainbow");
axes[0].set_title("Absolute values")
axes[1].set_title("Densities")
axes[2].set_title("Cumulative");
In [52]:
figure, axes = plt.subplots(1, 4, figsize=(12, 3))
hist3 = histogram([], 20, range=(160, 200))
for i, ax in enumerate(axes):
for height in np.random.normal(165 + 10 * i, 2.8, 10000):
hist3.fill(height)
hist3.plot(ax=ax);
print("After {0} batches: {1}".format(i, hist3))
figure.tight_layout()
In [53]:
heights1 = histogram(np.random.normal(169, 10, 100000), 50, range=(150, 200))
heights2 = histogram(np.random.normal(180, 11, 100000), 50, range=(150, 200))
total = heights1 + heights2
axis = heights1.plot(label="Women", color="red", alpha=0.5)
heights2.plot(label="Men", color="blue", alpha=0.5, ax=axis)
total.plot(label="All", color="gray", alpha=0.5, ax=axis)
axis.legend();
In [54]:
# Convert to pandas dataframe
hist.to_dataframe()
Out[54]:
In [55]:
# Works on xarray
import xarray as xr
arr = xr.DataArray(np.random.rand(10, 50, 100))
histogram(arr).plot(cmap="Reds_r", cmap_min=4744, cmap_max=5100, lw=1, edgecolor="red", show_values=True);
In [56]:
# Works on pandas dataseries
import pandas as pd
series = pd.Series(heights, name="height [cm]")
hist = histogram(series, title="Height distribution")
hist.plot()
hist
Out[56]:
In [57]:
json = hist.to_json() # add path argument to write it to file
json
Out[57]:
In [58]:
from physt.io import parse_json
hist = parse_json(json)
hist.plot()
hist
Out[58]: