Collection of basic tools for Python development. Install by
pip install cdxbasics
Tools for dynamic (animated) plotting in Jupyer/IPython. The aim of the toolkit is making it easy to develop visualization with matplotlib which dynamically updates, for example during training with machine learing kits such as tensorflow. This has been tested with Anaconda's JupyterHub and %matplotlib inline.
Some users reported that the package does not work in some versions of Jupyter. In this case, please try setting dynaplot.DynamicFig.MODE = 'canvas'. I appreciate if you let me know whether this resolved
the problem.
The package now contains a lazy method to manage updates. Instead of updating individual names, we recommend to simply remove the previous element and redraw. This is implemented as follows
fig is created, call fig.store() to return a element store.store +=.store.remove() to remove all old updates; create the renewed elements, and only then call fig.render() or fig.close().See the jupyter notebook notebooks/DynamicPlot.ipynb for some applications.
%matplotlib inline
import numpy as np
import cdxbasics.dynaplot as dynaplot
x = np.linspace(0,1,100)
pm = 0.2
fig = dynaplot.figure(col_size=10)
ax = fig.add_subplot()
ax2 = fig.add_subplot()
ax2.sharey(ax)
store = fig.store()
fig.render()
import time
for i in range(5):
y = np.random.random(size=(100,))
ax.set_title(f"Test {i}")
ax2.set_title(f"Test {i}")
store.remove() # delete all prviously stored elements
store += ax.plot(x,y,":", label=f"data {i}")
store += ax2.plot(x,y,"-",color="red", label=f"data {i}")
store += ax2.fill_between( x, y-pm, y+pm, color="blue", alpha=0.2 )
store += ax.legend()
fig.render()
time.sleep(1)
fig.close()
See example notebook for how to use the package for lines, confidence intervals, and 3D graphs.
The package lets you create sub plots without having to know the number of plots in advance: you do not need to specify rol, col, num when calling add_subplot. The underlying figure object will automatically arrange them on a grid for you.
# create figure
from cdxbasics.dynaplot import figure
fig = figure(col_size=4, row_size=4, col_num=3, title="Figure title")
# equivalent to matplotlib.figure
ax = fig.add_subplot() # no need to specify row,col,num
ax.plot( x, y )
ax = fig.add_subplot() # no need to specify row,col,num
ax.plot( x, y )
...
fig.next_row() # another row
ax = fig.add_subplot() # no need to specify row,col,num
ax.plot( x, y )
...
fig.render() # draws the plots
There are a number of other functions to aid plotting
figure() which returns a DynamicFig object:
Function to replace matplotlib.figure() which will defer creation of the figure until the first call of render(). The effect is that we no longer need to provide the total number of rows and columns in advance - i.e. you won't need to call the equivalent of fig.add_subplot(3,4,14) but can just call fig.add_subplot(). You can also pass a title argument.
Instead of figsize the function figure() accepts row_size, col_size and col_nums to dynamically generate an appropriate figure size.
Key member functions of DynamicFig are:
add_subplot() to add a new plot without having to specify the grid, e.g. you do not need to provide any arguments. Supports an additional title argument for plot titles.next_row() to skip to the next row.render() to draw the figure. When called the first time will create all the underlying matplotlib objects. Subsequent calls will re-draw the canvas if the figure was modified. See examples in https://github.com/hansbuehler/cdxbasics/blob/master/cdxbasics/notebooks/DynamicPlot.ipynbclose() to close the figure. If not called, Jupyter creates an unseemly second copy of the graph when the current cell is finished running.color_css4, color_base, color_tableau, color_xkcd:
Each function returns the i'th element of the respective matplotlib color table. The purpose is to simplify using consistent colors accross different plots.
Example:
fig = dynaplot.figure()
ax = fig.add_subplot()
# draw 10 lines in the first sub plot, and add a legend
for i in range(10):
ax.plot( x, y[i], color=color_css4(i), label=labels[i] )
ax.legend()
# draw 10 lines in the second sub plot. No legend needed as colors are shared with first plot
ax = fig.add_subplot()
for i in range(10):
ax.plot( x, z[i], color=color_css4(i) )
fig.render()
colors_css4, colors_base, colors_tableau, colors_xkcd:
Generator versions of the color_ functions.
The DynamicFig object returned by dynaplot.figure() will keep track of all function calls and other operations, and will defer them until the first time render(). It does this so it can figure out the desired layout before actually creating any plots. Each deferred function call in turn returns a deferring object. Read the Python comments in deferred.py for implementation details.
A number of simple extensions to standard dictionaries which allow accessing any element of the dictionary with "." notation. The purpose is to create a functional-programming style method of generating complex objects.
from cdxbasics.prettydict import PrettyDict
pdct = PrettyDict(z=1)
pdct['a'] = 1 # standard dictionary write access
pdct.b = 2 # pretty write access
_ = pdct.b # read access
_ = pdct("c",3) # short cut for pdct.get("c",3)
There are three versions:
PrettyDict:
Pretty version of standard dictionary.PrettyOrderedDict:
Pretty version of ordered dictionary.PrettySortedDict:
Pretty version of sorted dictionary."Pretty" objects also allow assigning bona fide member functions by a simple semantic of the form:
def mult_b( self, x ):
return self.b * x
pdct = mult_a
Calling pdct.mult_a(3) with above config will return 6 as expected. This only works when using the member synthax for assigning values
to a pretty dictionary; if the standard [] operator is used then functions will be assigned to the dictionary as usual, hence they are static members of the object.
The reason for this is as follows: consider
def mult( a, b ):
return a*b
pdct.mult = mult
mult(3,4) --> produces am error as three arguments as are passed if we count 'self'
In this case, use:
pdct['mult'] = mult
pdct.mult(3,4) --> 12
Tooling for setting up program-wide configuration. Aimed at machine learning programs to ensure consistency of code accross experimentation.
from cdxbasics.config import Config
config = Config()
Key features
Set data with both dictionary and member notation:
config = Config()
config['features'] = [ 'time', 'spot' ]
config.weights = [ 1, 2, 3 ]
Create sub configurations with member notation
config.network.depth = 10
config.network.activation = 'relu'
config.network.width = 100
This is equivalent to
config.network = Config()
config.network.depth = 10
config.network.activation = 'relu'
config.network.width = 100
When reading the value of a key from config, config.__call__() uses a default value, and a cast type. It first attempts to find key in the config.
key is found, it casts the value provided for key using the cast type and returned.key is not found, then the default value will be cast using cast type and returned.The function also takes a help text which allows providing live information on what variable are read from the config. The latter is used by the function usage_report() which therefore provides live documentation of the code which uses the config object.
class Network(object):
def __init__( self, config ):
# read top level parameters
self.features = config("features", [], list, "Features for the agent" )
self.weights = config("weights", [], np.asarray, "Weigths for the agent", help_default="no initial weights")
config.done() # see below
In above example any data provided for they keywords weigths will be cast using numpy.asarray.
Further parameters of () are the help text, plus ability to provide text versions of the default with help_default (e.g. if the default value is complex), and the cast operator with help_cast (again if the
respective operation is complex).
Important: the () operator does not have a default value unless specified. If no default value is specified, and the key is not found, then a KeyError is generated.
You can read sub-configurations with the previsouly introduced member notation:
self.activation = config.network("activation", "relu", str, "Activation function for the network")
An alternative is the explicit:
network = config.network
self.depth = network('depth', 10000, int, "Depth for the network")
We can impose simple restrictions to any values read from a config. To this end, import the respective type operators:
from cdxbasics.config import Int, Float
One-sided restriction:
# example enforcing simple conditions
self.width = network('width', 100, Int>3, "Width for the network")
Restrictions on both sides of a scalar:
# example encorcing two-sided conditions
self.percentage = network('percentage', 0.5, ( Float >= 0. ) & ( Float <= 1.), "A percentage")
Enforce the value being a member of a list:
# example ensuring a returned type is from a list
self.ntype = network('ntype', 'fastforward', ['fastforward','recurrent','lstm'], "Type of network")
We can allow a returned value to be one of several casting types by using tuples. The most common use case is that None is a valid value for a config, too. For example, assume that the name of the network model should be a string or None. This is implemented as
# example allowing either None or a string
self.keras_name = network('name', None, (None, str), "Keras name of the network model")
We can combine conditional expressions with the tuple notation:
# example allowing either None or a positive int
self.batch_size = network('batch_size', None, (None, Int>0), "Batch size or None for TensorFlow's default 32", help_cast="Positive integer, or None")
A common issue when using dictionary-based code is that we might misspell one of the parameters. Unless this is a mandatory parameter we might not notice that we have not actually changed its value in the code below.
To check that all values of config are read use done()
config.done() # checks that we have read all keywords.
It will alert you if there are keywords or children which haven't been read. Most likely, those will be typos. Consider the following example where width is misspelled in our config:
class Network(object):
def __init__( self, config ):
# read top level parameters
self.depth = config("depth", 1, Int>=1, "Depth of the network")
self.width = config("width", 3, Int>=1, "Width of the network")
self.activaton = config("activation", "relu", help="Activation function", help_cast="String with the function name, or function")
config.done() # <-- test that all members of config where read
config = Config()
config.features = ['time', 'spot']
config.network.depth = 10
config.network.activation = 'relu'
config.network.widht = 100 # (intentional typo)
n = Network(config.network)
Since width was misspelled in setting up the config, you will get a warning to this end:
Error closing 'config.network': the following config arguments were not read: ['widht']
Summary of all variables read from this object:
config.network['activation'] = relu # Activation function; default: relu
config.network['depth'] = 10 # Depth of the network; default: 1
config.network['width'] = 3 # Width of the network; default: 3
Note that you can also call done() at top level:
class Network(object):
def __init__( self, config ):
# read top level parameters
self.depth = config("depth", 1, Int>=1, "Depth of the network")
self.width = config("width", 3, Int>=1, "Width of the network")
self.activaton = config("activation", "relu", help="Activation function", help_cast="String with the function name, or function")
config = Config()
config.features = ['time', 'spot']
config.network.depth = 10
config.network.activation = 'relu'
config.network.widht = 100 # (intentional typo)
n = Network(config.network)
test_features = config("features", [], list, "Features for my network")
config.done()
produces
ERROR:x:Error closing 'config.network': the following config arguments were not read: ['widht']
Summary of all variables read from this object:
config.network['activation'] = relu # Activation function; default: relu
config.network['depth'] = 10 # Depth of the network; default: 1
config.network['width'] = 3 # Width of the network; default: 3
#
config['features'] = ['time', 'spot'] # Default: 2
You can check the status of the use of the config by using the not_done property.
You can also detach a child config, which allows you to store it for later use without triggering done() errors:
def read_config( self, confg ):
...
self.config_training = config.training.detach()
config.done()
detach() will mark he original child as 'done'. Therefore, we will need to call done() again, when we finished processing the detached child:
def training(self)
epochs = self.config_training("epochs", 100, int, "Epochs for training")
batch_size = self.config_training("batch_size", None, help="Batch size. Use None for default of 32" )
self.config_training.done()
Use copy() to make a bona fide copy of a child, without marking the source child as 'done'. copy() will return a config which shares the same status as the source object. If you want an "unused" copy, use clean_copy(). A virtual clone is created via clone(). A cloned config stores information on usage in the same place for the original object. This is also the semantic of the copy constructor.
Once your program ran, you can read the summary of all values, their defaults, and their help texts.
print( config.usage_report( with_cast=True ) )
Prints:
config.network['activation'] = relu # (str) Activation function for the network; default: relu
config.network['depth'] = 10 # (int) Depth for the network; default: 10000
config.network['width'] = 100 # (int>3) Width for the network; default: 100
config.network['percentage'] = 0.5 # (float>=0. and float<=1.) Width for the network; default: 0.5
config.network['ntype'] = 'fastforward' # (['fastforward','recurrent','lstm']) Type of network; default 'fastforward'
config.training['batch_size'] = None # () Batch size. Use None for default of 32; default: None
config.training['epochs'] = 100 # (int) Epochs for training; default: 100
config['features'] = ['time', 'spot'] # (list) Features for the agent; default: []
config['weights'] = [1 2 3] # (asarray) Weigths for the agent; default: no initial weights
def create_network( depth=20, activation="relu", width=4 ):
...
We may use
create_network( **config.network )
However, there is no magic - this function will mark all direct members (not children) as 'done' and will not record the default values of the function create_network. Therefore usage_report will be somewhat useless. This method will still catch unused variables as "unexpected keyword arguments".
Another common use case is that we wish to cache some process in a complex operation. Assuming that the config describes all relevant parameters
we can use config.unique_id() to obtain a unique hash ID for the given config.
This can be used, for example, as file name for caching. See also cdxbasics.subdir below.
The Config class can be used to improve kwargs handling.
Assume we have
def f(**kwargs):
a = kwargs.get("difficult_name", 10)
b = kwargs.get("b", 20)
We run the usual risk of somebody mispronouncing the parameter name which we would never know. Therefore we may improve upon the above with
def f(**kwargs):
kwargs = Config(kwargs)
a = kwargs("difficult_name", 10)
b = kwargs("b", 20)
kwargs.done()
If now a user calls f with a misspelled config(difficlt_name=5) an error will be raised.
Another pattern is to allow both config and kwargs:
def f( config=Config(), **kwargs):
kwargs = config.detach.update(kwargs)
a = kwargs("difficult_name", 10)
b = kwargs("b", 20)
kwargs.done()
Tools for defensive programming a'la the C++ ASSERT/VERIFY macros. Aim is to provide one line validation of inputs to functions with intelligible error messages:
from cdxbasics.logger import Logger
_log = Logger(__file__)
...
def some_function( a, ...):
_log.verify( a==1, "'a' is not one but %s", a)
_log.warn_if( a!=1, "'a' was not one but %s", a)
Exceptions independent of logging level
verify( cond, text, *args, **kwargs )
If cond is not met, raise an exception with util.fmt( text, *args, **kwargs ). This is the Python version of C++ VERIFY
throw_if(cond, text, *args, **kwargs )
If cond is met, raise an exception with util.fmt( text, *args, **kwargs )
throw( text, *args, **kwargs )
Just throw an exception with util.fmt( text, *args, **kwargs )
Unconditional logging
debug( text, *args, **kwargs )
info( text, *args, **kwargs )
warning( text, *args, **kwargs )
error( text, *args, **kwargs )
critical( text, *args, **kwargs )
throw( text, *args, **kwargs )
Verify-conditional functions
# raise an exception if 'cond' is not True
verify( cond, text, *args, **kwargs )
# print log message of respective level if 'cond' is not True
verify_debug( cond, text, *args, **kwargs )
verify_info( cond, text, *args, **kwargs )
verify_warning( cond, text, *args, **kwargs )
If-conditional functions
# raise an exception if 'cond' is True
throw_if( cond, text, *args, **kwargs )
# write log message if 'cond' is True
debug_if( cond, text, *args, **kwargs )
info_if( cond, text, *args, **kwargs )
warning_if( cond, text, *args, **kwargs )
# print message if 'cond' is True
prnt_if( cond, text, *args, **kwargs ) # with EOL
write_if( cond, text, *args, **kwargs ) # without EOL
A few tools to handle file i/o in a transparent way. The key idea is to provide transparent, concise pickle access to the file system in a manner similar to dictionary access - hence core file names are referred to as 'keys'. Files managed by subdir all have the same extension. From 0.2.60 SubDir supports different file formats specified with the fmt= keyword to SubDir:
jsonpickle package. Default extension 'jpck'. The advantage of this format over PICKLE is that it is somewhat human-readable. However, jsonpickle uses compressed formats for complex objects such as numpy arrays, hence readablility is somewhat limited. It comes at cost of slower writing speeds.cdxbasics.util.plain() to convert objects into plain Python objects before using json to write them. That means that deserialized data does not have the correct object structure. However, such files are much easier to read.You can create directories using the SubDir class. Simply write
from cdxbasics.subdir import SubDir
subdir = SubDir("my_directory") # relative to current working directory
subdir = SubDir("./my_directory") # relative to current working directory
subdir = SubDir("~/my_directory") # relative to home directory
subdir = SubDir("!/my_directory") # relative to default temp directory
You can specify a parent for relative path names:
from cdxbasics.subdir import SubDir
subdir = SubDir("my_directory", "~") # relative to home directory
subdir2 = SubDir("my_directory", subdir) # subdir2 is relative to `subdir`
Change the extension to bin
from cdxbasics.subdir import SubDir
subdir = SubDir("~/my_directory;*.bin")
subdir = SubDir("~/my_directory", ext="bin")
subdir = SubDir("my_directory", "~", ext="bin")
You can turn off extension management by setting the extension to "":
from cdxbasics.subdir import SubDir
subdir = SubDir("~/my_directory", ext="")
You may specify the file format; in this case the extension will be automaticall set to pck, jpck or json, respectively. See discussion above about the relative merits of each format:
from cdxbasics.subdir import SubDir
subdir = SubDir("~/my_directory", fmt=SubDir.PICKLE)
subdir = SubDir("~/my_directory", fmt=SubDir.JSON_PICKLE)
subdir = SubDir("~/my_directory", fmt=SubDir.JSON_PLAIN)
You can also use the () operator to generate sub directories. This operator is overloaded: for a single argument, it creates a relative sub-directory:
from cdxbasics.subdir import SubDir
parent = SubDir("~/parent")
subdir = parent("subdir") # shares extension and format with parent
subdir = parent("subdir", ext="bin", fmt=SubDir.PICKLE) # change extension and format
Be aware that when the operator () is called with two arguments, then it reads files; see below.
You can obtain a list of all sub directories in a directory by using subDirs().
To read the data contained in a file 'file' in our subdirectory with the extension used for the sub directory, use either of the following
data = subdir.read("file") # returns the default if file is not found
data = subdir.read("file", default=None) # returns the default if file is not found
This function will return None or the default if 'file' does not exist with the respective extension. You can make it throw an error by calling subdir.read("file", throwOnError=True) instead.
You may specify a different extension:
data = subdir.read("file", ext="bin")
Specifying a different format for read does not change the extension automatically, hence you may want to set this explicitly at the same time:
data = subdir.read("file", ext="json", fmt=Subdir.JSON_PLAIN )
You can also use the () operator, in which case you must specify a default value (if you don't, then the operator will return a sub directory):
data = subdir("file", None) # returns None if file is not found
You can also use both member and item notation to access files. In this case, though, an error will be thrown if the file does not exist
data = subdir.file # raises AtttributeError if file is not found
data = subdir['file'] # raises KeyError if file is not found
You can read a range of files in one function call:
data = subdir.read( ["file1", "file2"] )
Finally, you can also iterate through all existing files:
for file in subdir:
data = subdir.read(file)
...
To obtain a list of all files in our directory which have the correct extension, use files() or keys().
To write data, use any of
subdir.write("file", data)
subdir.file = data
subdir['file'] = data
You may specifify different extensions:
subdir.write("file", data, ext="bin)
You can also specify the file format. Note that this will not automatically change the extension, so you may want to set this at the same time:
subdir.write("file", data, fmt=SubDir.JSON_PLAIN, ext="json")
To write several files at once, write
subdir.write(["file1", "file"], [data1, data2])
Note that when writing to an object, subdir will first write to a temporary file, and then rename this file into the target file name. The temporary file name is a util.uniqueHash48 generated from the target file name, current time, process and thread ID, as well as the machines's UUID. This is done to reduce collisions between processes/machines accessing the same files. It does not remove collision risk entirely, though.
SubDir handles core file names for you as "keys" and adds directories and extensions as required. You can obtain the full qualified filename given a "key" by calling fullFileName()
or fullKeyName().
From 0.2.64 SubDir supports versioned files. If versions are used, then they must be used for both reading and writing.
If version= is provided, then write() will write it in a block ahead of the main content of the file. In case of the PICKLE format, this is a byte string. In case of JSON_PLAIN and JSON_PICKLE this is line of text starting with # ahead of the file. (Note that this violates
the JSON file format.)
The point of writing short block ahead of the main data is that read() can read this version information back quickly before attempting to read the entire file. read() does attempt so if its called with version= as well. In this case it will compare the read version with the provided version, and only return the main content of the file if versions match.
Use is_version() to check whether a given file has a specific version. This function only reads the information required to obtain the information and will be much faster than reading the whole file if the file size is big.
Examples:
Writing a versioned file:
from cdxbasics.subdir import sub_dir
sub_dir = SubDir("!/test_version)
sub_dir.write("test", [1,2,3], version="0.0.1" )
To read [1,2,3] from "test" we need to use the correct version:
_ = sub_dir.read("test", version="0.0.1")
We now try to use:
_ = sub_dir.read("test", version="0.0.2")
This fails reading [1,2,3] from "test" as the versions do not match.
Moreoever, read() will then attempt to delete the file "test". This can be turned off
with the keyword delete_wrong_version.
We do not do that below, so the file will be deleted, and read() will then return the default value None.
You can ignoore the version used to write a file by using * as version:
_ = sub_dir.read("test", version="*")
Note that reading files which have been written with a version back without version= keyword will fail because SubDir will only append additional information to the chosen file format if required.
To test existence of 'file' in a directory, use one of
subdir.exist('file')
'file' in subdir
To delete a 'file', use any of the following:
subdir.delete(file)
del subdir.file
del subdir['file']
All of these are silent, and will not throw errors if 'file' does not exist. In order to throw an error use
subdir.delete(file, raiseOnError=True)
deleteAllKeys: delete all files in the directory, but do not delete sub directories or files with extensions different to our own.deleteAllContent: delete all files with our extension, and all sub directories.eraseEverything: delete everythingA system wide resource lock using a simplistic but robust implementation via a file lock.
The FileLock represents a lock implemented using a file with exclusive access under both Linux and Windows. The filename supports short-hand root directory references to the current temp directory (!/) or the user directory (~/).
Simplest form - will throw an exception if the lock could not be attained:
from cdxbasics.filelock import FileLock
fl = FileLock("!/resource.lock", acquire=True, wait=False)
With timeout up to 5*10 seconds:
from cdxbasics.filelock import FileLock
fl = FileLock("!/resource.lock", acquire=True, wait=True, timeout_seconds=5, timeout_repeat=10 )
Wait forever
from cdxbasics.filelock import FileLock
fl = FileLock("!/resource.lock", acquire=True, wait=True, timeout_seconds=5, timeout_repeat=None )
A more verbose use case is to not automatically aqcuire the lock upon construction.
In this case call acquire() to obtain a lock:
from cdxbasics.filelock import FileLock
fl = FileLock("!/resource.lock")
if not fl.acquire():
print("Failed to acquire lock")
return
...
fl.release()
The lock will keep count of the number of times acquire and release are called, respectively. The number of current (net) acquisitions can be obtained using the num_acquisitions property.
Note that a FileLock will by default release the lock upon destruction of the lock. However, due to Python's garbage collection that even might not be immediate. To enforce releasing a lock use release(). This is handled more elegantly by using it as a context manager:
A better method is to use FileLock is as a context manager in which case the lock will be released upon leaving the while block.
Note that unless acquire is set to True the lock is not obtained.
from cdxbasics.filelock import FileLock
with FileLock("!/resource.lock", acquire=True):
...
...
To debug usage of the lock one may use a Context object from the verbose sub-module. To display all verbose information, pass None:
from cdxbasics.filelock import FileLock
fl = FileLock("!/resource.lock", aqcuire=True, verbose=None )
A collection of utility functions.
uniqueHash( *kargs, **kwargs )
uniqueHash32( *kargs, **kwargs )
uniqueHash48( *kargs, **kwargs )
uniqueHash64( *kargs, **kwargs )
Each of these functions returns a unique hash key for the arguments provided for the respective function. The functions *32,*48,*64 return hashes of the respective length, while uniqueHash returns the hashes of standard length. These functions will make an effort to robustify the hashes against Python particulars: for example, dictionaries are hashed with sorted keys.
These functions will ignore all dictionary or object members starting with "_". They also will by default not hash functions or properties.
This is sometimes undesitable, for example when functions are configuration elements:
config = Config()
config.f = lambda x : x**2
To change default behaviour, use
myUniqueHash = uniqueHashExt( length = 48, parse_functions = True, parse_underscore = "protected")
The returned function myUniqueHash will parse functions, and will also include protect members.
A simple enum-type class to help implement a standard caching pattern. It implements the following decision matrix
| on | off | update | clear | readonly | |
|---|---|---|---|---|---|
| load cache from disk if exists | x | - | - | - | x |
| write updates to di.sk | x | - | x | - | - |
| delete existing object | - | - | - | x | - |
| delete existing object if incompatible | x | - | x | x | - |
(For debugging purposes, an additional mode gen behaves like on except that it does not delete files with the wrong version.)
Typically, the user is allowed to set the desired CacheMode using a Config element. The corresponding CacheMode object then implements the properties read, write, delete and del_incomp.
Caching of versioned functions with the above logic is implemented in cdxbasics.cached, see below. It used cdxbasics.version to determine the version of a function, and all its dependencies.
Prototype code is to be implemented as follows:
from cdxbasics.util import CacheMode, uniqueHash48
from cdxbasics.subdir import SubDir
from cdxbasics.version import version
@version("0.0.1")
def compute( *kargs, **kwargs ):
... my function
return ...
def compute_cached( *kargs, cache_mode : CacheMode, cache_dir : SubDir, **kargs ):
# compute a unique hash from the input parameters.
# the default method used here may not work for all parameter types
# (most notable, uniqueHash48 will ignore members of any objects starting with '_'; see above)
unique_id = unqiueHash48( kargs, kwarg )
# obtain a unique summary of the version of this function
# and all its dependents.
version_id = compute.version.unique_id48
# delete existing cache
# if requested by the user
if cache_mode.delete:
cache_dir.delete(unique_id)
# attempt to read cache
# by providing a version we ensure that changes to the function
# code will trigger an update of the cache by deleting any
# existing files with different versions
if cache_mode.read:
ret = cache_dir.read(unique_id,
default=None,
version=version_id,
delete_wrong_version=cache_model.del_incomp
)
if not ret is None:
return ret
# compute new object
# using main function
ret = compute( *kargs, **kwargs )
# write new object to disk if so desired
# include version
if cache_mode.write:
cache_dir.write(unique_id, ret, version=version_id )
return ret
A decorator with associated behaviour is being built.
A simple utility class to manage printing in a given line with carriage returns (\r).
Essentially, it keeps track of the length what was printed so far at the current line. If a \r is encountered it will clear the rest of the line to avoid having residual text from the previous line.
Example 1 (how to use \r and \n)
write = WriteLine("Initializing...")
import time
for i in range(10):
time.sleep(1)
write("\rRunning %g%% ...", round(float(i+1)/float(10)*100,0))
write(" done.\nProcess finished.\n")
Example 2 (line length is getting shorter)
write = WriteLine("Initializing...")
import time
for i in range(10):
time.sleep(1)
write("\r" + ("#" * (9-i)))
write("\rProcess finished.\n")
fmt(): C++ style format function.
plain(): converts most combinations of standards elements or objects into plain list/dict structures.
isAtomic(): whether something is string, float, int, bool or date.
isFloat(): whether something is a float, including a numpy float.
isFunction(): whether something is some function.
bind(): simple shortcut to bind function parameters, e.g.
def f(a, b, c):
pass
f_a = bind(f, a=1)
fmt_list() returns a nicely formatted list, e.g. fmt_list([1,2,3]) returns 1, 2 and 3.
fmt_dict() returns a nicely formatted dictionary, e.g. fmt_dict({'a':1,'b':'test'}) returns a: 1, b: test.
fmt_seconds() returns string for seconds, e.g. fmt_seconds(10) returns 10s while fmt_seconds(61) returns 1:00.
fmt_digits() inserts ',' or another separator in thousands, i.e. fmt_digits(12345) returns 12,345.
fmt_big_number() converts a large integer into an abbreviated string with terminating K, M, B, T as appropriate, using base 10. For example fmt_big_number(12345) returns 12.35K.
fmt_big_byte_number() converts a large integer into an abbreviated string with terminating K, M, G, T as appropriate, here using base 16. For example fmt_big_byte_number(12345) returns 12.06K.
fmt_date() returns a date string in natural order e.g. YYYY-MM-DD.
fmt_time() returns a time string in natural order HH:MM:SS. The colon can be changed into another character if required, e.g. for file names.
fmt_datetime() returns a datetime string in natural order e.g. YYYY-MM-DD HH:SS. It returns the respective simplification if just a date or time is passed instead of a datetime.
fmt_filename() returns a valid filename for both Windows and Linux by replacing unsupported characters with alternatives. Instead of our default alternatives you can pass a dictionary of your own.
is_jupyter() tries to assess whether the current environment is a jupyer IPython environment.
This is experimental as it appears there is no safe way to do this. The current implemenentation checks whether the command which started the current process contains the string jupyter.
A small number of statistical numpy functions which take a weight vector (distribution) into account, namely
mean(P,x,axis) computes the mean of x using the distribution P. If P is None, it returns numpy.mean(x,axis).
var(P,x,axis) computes the variance of x using the distribution P. If P is None, it returns numpy.var(x,axis).
std(P,x,axis) computes the standard deviation of x using the distribution P. If P is None, it returns numpy.std(x,axis).
err(P,x,axis) computes the standard error of x using the distribution P. If P is None, it returns numpy.std(x,axis)/sqrt(x.shape[axis]).
quantile(P,x,quantiles,axis) computes P-quantiles of x. If P is None, it returns numpy.quantile(x,quantiles,axis).
median(P,x,axis) computes the P-median of x. If P is None, it returns numpy.median(x,axis).
mad(P,x,axis) computes the median absolute deviation of x with respect to the distribution P. Note that mad returned by this function is scaled to be an estimator of std.
Two further functions are used to compute binned statistics:
mean_bins(x,bins,axis,P) computes the means of x over equidistant bins using the distribition P.mean_std_bins(x,bins,axis,P) computes the means and standard deviations of x over equidistant bins using the distribition P.For derivative pricing:
np_european(...) computes European option prices and greeks.Hard efficency numpy file i/io functions. They offer unbuffereed reading/writing numpy arrays in their native byte form from and to disk.
tofile(file,array) writes a numpy array in an efficient native binary format to file without buffering. The unbuffered 2GB Linux write limit is circumvented.fromfile(file, dtype) reads from a numpy binary file into a new numpy array given a known dtype. The unbuffered 2GB Linux read limit is circumvented.readinto(file, array) reads file into an existing target array.readfromfile(file, target) reads file into an existing numpy array, or into a new one.The verbose interface has changed in 0.2.36
Since 0.2.95 verbose is using CRMan to manage messages containing '\r'.
This module provides the Context utility class for printing 'verbose' information, with indentation depending on the detail level.
The basic idea is that the root context has level 0, with increasing levels for sub-contexts. When printing information, we can (a) limit printing up to a given level and (b) automatically indent the output to reflect the current level of detail.
Context model, and define its verbosity in its constructor, e.g. all, none or a number. A negative number means that no outout will be generated (quiet), while None means all output will be printed (all). Sub-contexts inherent verbosity from their parents.stdout use write().report(). You can also use the overloaded call operator.sub() or use the overloaded call operator.Here is an example:
from cdxbasics.verbose import Context, quiet
def f_sub( num=10, context = quiet ):
context.report(0, "Entering loop")
for i in range(num):
context.report(1, "Number %ld", i)
def f_main( context = quiet ):
context.write( "First step" )
# ... do something
context.report( 1, "Intermediate step 1" )
context.report( 1, "Intermediate step 2\nwith newlines" )
# ... do something
f_sub( context=context(1) ) # call function f_sub with a sub-context
# ... do something
context.write( "Final step" )
print("Verbose=1")
context = Context(1)
f_main(context)
print("\nVerbose=2")
context = Context(2)
f_main(context)
print("\nVerbose='all'")
context = Context('all')
f_main(context)
print("\nVerbose='quiet'")
context = Context('quiet')
f_main(context)
Returns
Verbose=1
00: First step
01: Intermediate step 1
01: Intermediate step 2
01: with newlines
01: Entering loop
00: Final step
Verbose=2
00: First step
01: Intermediate step 1
01: Intermediate step 2
01: with newlines
01: Entering loop
02: Number 0
02: Number 1
02: Number 2
02: Number 3
02: Number 4
02: Number 5
02: Number 6
02: Number 7
02: Number 8
02: Number 9
00: Final step
Verbose='all'
00: First step
01: Intermediate step 1
01: Intermediate step 2
01: with newlines
01: Entering loop
02: Number 0
02: Number 1
02: Number 2
02: Number 3
02: Number 4
02: Number 5
02: Number 6
02: Number 7
02: Number 8
02: Number 9
00: Final step
Verbose='quiet'
The purpose of initializing functions usually with quiet is that they can be used accross different contexts without printing anything by default.
Framework to keep track of versions of functions, and their dependencies. Main use case is a data pipeline where a changes in versions down a dependency tree should trigger an update of the "full" version of the respective top level calculation.
The framework relies on the @version decorator which works for both classes and functions.
Applied to either a function or class it will add a member version which has the following properties:
version.input: the input version as defined with @version.version.full: a fully qualified version with all dependent functions and classes in human readable form.version.unique_id48, version.unique_id64: unique hashes of version.full of 48 or 64 characters, respectively. You can use the function version.unique_id() to compute hash IDs of any length.version.dependencies: a hierarchical list of dependencies for systematic inspection.Note that dependencies and all other information will only be resolved upon a first call to any of these properties.
Usage is straight forward:
from cdxbasics.version import version
@version("0.0.1")
def f(x):
return x
print( f.version.input ) --> 0.0.1
print( f.version.full ) --> 0.0.1
Dependencies are declared with the dependencies keyword:
@version("0.0.2", dependencies=[f])
def g(x):
return f(x)
print( g.version.input ) --> 0.0.2
print( g.version.full ) --> 0.0.2 { f: 0.0.01 }
You have access to version from within the function:
@version("0.0.2", dependencies=[f])
def g(x):
print(g.version.full) --> 0.0.2 { f: 0.0.01 }
return f(x)
This works with classes, too:
@version("0.0.3", dependencies=[f] )
class A(object):
def h(self, x):
return f(x)
print( A.version.input ) --> 0.0.3
print( A.version.full ) --> 0.0.3 { f: 0.0.01 }
a = A()
print( a.version.input ) --> 0.0.3
print( a.version.full ) --> 0.0.3 { f: 0.0.01 }
You can also use strings to refer to dependencies. This functionality depends on visibility of the referred dependencies by the function in the function's __global__ scope. Currently, it does not work with local function definitions.
@version("0.0.4", dependencies=['f'])
def r(x)
return x
print( r.version.full ) --> 0.0.4 { f: 0.0.01 }
Dependencies on base classes are automatic:
@version("0.0.1")
class A(object):
pass
@version("0.0.2")
class B(A):
pass
print( A.version.full ) --> 0.0.1
print( B.version.full ) --> 0.0.2 { A: 0.0.1 }
As a direct use case you can provide version.unqiue_id48 to the version keyword of SubDir.read and SubDir.write. The latter will write the version string into the output file. The former will then read it back (by reading a small block of data), and check that the version written to the file matches the current version. If not, the file will be considered invalid; depending on the parameters to read the function will either return a default value, or will throw an exception.
from cdxbasics.util import uniqueHash48
from cdxbasics.version import version
from cdxbasics.subdir import SubDir
@version("0.0.1")
def f( path, x, y, z ):
unique_file = uniqueHash48( x,y,z )
unique_ver = f.version.unique_id48
subdir = SubDir(path)
data = subdir.read( unique_file, None, version=unique_ver )
if not data is None:
return data
data = compute(x,y,z)
subdir.write( unique_file, data, version=unique_ver )
return data
This functionality is used in cdxbasics.cached, below.
Framework for caching versioned functions.
The core tennets are:
Cached functions have versions. If the version of a cached file differs from the current function version, do not use it. Versioning is implemented using cdxbasics.version.version.
Ability to control the use of the cache dynamically. The user can chose to use, ignore or update the cache. This is controlled using cdxbasics.util.CacheMode.
Control extends to dependent functions, i.e. we can force an update of a top level function if a dependent function needs an update.
Transparent tracing: by default caching will provide detailled information about what is happening. This can be controlled using the cache_verbose parameter to Cache, which uses cdxbasics.verbose.Context.
Here are some examples for managing caching:
from cdxbasics.cached import version, cached, Cache
# the function f,g are not cached but have versions
@version("0.0.1")
def f(x,y):
return x*y
@version("0.0.2", dependencies=[f])
def g(x,y):
return f(-x,y)
# the cached function 'my_func' depends on g and therefore also on f
@cached("0.0.3", dependencies=[g])
def my_func( x,y, cache=None ):
return g(2*x,y)
# the casched function 'my_big_func' depends on 'my_func' and therefore also on g,f
@cached("0.0.4", dependencies=[my_func])
def my_big_func(x,y,z, cache=None ):
r = my_func(x,y,cache=cache)
return r*z
# test versioning
print("Version", my_big_func.version) # --> 0.0.4 { my_func: 0.0.3 { g: 0.0.2 { f: 0.0.1 } } }
# function call without caching
r = my_big_func(2,3,4) # does not generate a cache: 'cache' argument not provided
# delete existing caches
print("\nDelete existing cache")
cache = Cache(cache_mode="clear") # path defaults to !/.cached (e.g. tempdir/.cached)
r = my_big_func(2,3,4,cache=cache) # generates the cache for my_big_func and my_func
# test caching
print("\nGenerate new cache")
cache = Cache() # path defaults to !/.cached (e.g. tempdir/.cached)
r = my_big_func(2,3,4,cache=cache) # generates the cache for my_big_func and my_func
print("\nReading cache")
r = my_big_func(2,3,4,cache=cache) # reads cache for my_big_func
# update
print("\nUpdating all cached objects")
cache_u = Cache(cache_mode="update")
r = my_big_func(2,3,4,cache=cache_u) # updates the caches for my_big_func, my_func
print("\nReading cache")
r = my_big_func(2,3,4,cache=cache) # reads cache for my_big_func
# update only top level cache
print("\nUpdating only 'my_big_func'")
cache_lu = Cache(cache_mode="on", update=[my_big_func] )
r = my_big_func(2,3,4,cache=cache_lu) # updates the cache for my_big_func using the cache for my_func
print("\nReading cache")
r = my_big_func(2,3,4,cache=cache) # reads cached my_big_func
Here is the output of above code block: it also shows the aforementioned transparent trading.
Version 0.0.4 { my_func: 0.0.3 { g: 0.0.2 { f: 0.0.1 } } }
Delete existing cache
00: Deleted existing 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
01: Deleted existing 'my_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_func_47317c662192f51fddd527cb89369f77c547fc58cca962d7.pck
Generate new cache
01: Wrote 'my_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_func_47317c662192f51fddd527cb89369f77c547fc58cca962d7.pck
00: Wrote 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
Reading cache
00: Successfully read cache for 'my_big_func' from 'C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck'
Updating cache
00: Deleted existing 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
01: Deleted existing 'my_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_func_47317c662192f51fddd527cb89369f77c547fc58cca962d7.pck
01: Wrote 'my_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_func_47317c662192f51fddd527cb89369f77c547fc58cca962d7.pck
00: Wrote 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
Reading cache
00: Successfully read cache for 'my_big_func' from 'C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck'
Updating only 'my_big_func'
00: Caching mode for function 'my_big_func' set to 'update' as it depends on 'my_big_func'
00: Deleted existing 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
01: Successfully read cache for 'my_func' from 'C:/Users/hansb/AppData/Local/Temp/.cache/my_func_47317c662192f51fddd527cb89369f77c547fc58cca962d7.pck'
00: Wrote 'my_big_func' cache C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck
Reading cache
00: Successfully read cache for 'my_big_func' from 'C:/Users/hansb/AppData/Local/Temp/.cache/my_big_func_6ac240bc128ec33ca37c17c5aab243e46b976893ccf0c40a.pck'
FAQs
Basic Python tools
We found that cdxbasics demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.