Repository: mkalioby/leopards
Branch: main
Commit: 3e86850c1f88
Files: 13
Total size: 41.2 KB
Directory structure:
gitextract_8g1d786o/
├── .github/
│ └── workflows/
│ └── workflow.yml
├── .gitignore
├── LICENSE
├── README.md
├── USAGE.md
├── leopards/
│ ├── Q.py
│ ├── Query.py
│ └── __init__.py
├── setup.py
├── tests/
│ ├── __init__.py
│ ├── test_file.py
│ └── test_leopards.py
└── tox.ini
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/workflow.yml
================================================
name: test
on: [push]
env:
MODE: Test
jobs:
tests:
name: "Python ${{ matrix.python-version }} on ${{ matrix.os }}"
runs-on: "${{ matrix.os }}"
strategy:
fail-fast: false
matrix:
os:
- ubuntu-latest
- macos-latest
- windows-latest
python-version:
- "3.8"
- "3.9"
- "3.10"
- "3.11"
- "3.12"
- "3.13"
steps:
# Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
- name: Checkout code
uses: actions/checkout@v2
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: "${{ matrix.python-version }}"
- name: Install requirements
run: |
pip install pytest pytest-cov
- name: Run tests
run: |
pytest --cov=leopards tests/ --cov-report term-missing
================================================
FILE: .gitignore
================================================
env/
.idea/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2022 Mohamed El-Kalioby
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Leopards
[](https://badge.fury.io/py/leopards)
[](https://img.shields.io/pypi/pyversions/leopards.svg)
Leopards is a way to query list of dictionaries or objects as if you are filtering in DBMS.
You can get dicts/objects that are matched by OR, AND or NOT or all of them.
As you can see in the comparison they are much faster than Pandas.
## Installation
```shell
pip install leopards
```
## Usage
```python
from leopards import Q
l = [{"name":"John","age":"16"}, {"name":"Mike","age":"19"},{"name":"Sarah","age":"21"}]
filtered= Q(l,{'name__contains':"k", "age__lt":20})
print(list(filtered))
```
output
```python
[{'name': 'Mike', 'age': '19'}]
```
The above filtration can be written as
```python
from leopards import Q
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"}]
filtered = Q(l, name__contains="k", age__lt=20)
```
**Notes:**
1. `Q` returns an iterator which can be converted to a list by calling `list`.
2. Even though, age was `str` in the dict, as the value of in the query dict was `int`, Leopards converted the value in dict automatically to match the query data type. This behaviour can be stopped by passing `False` to `convert_types` parameter.
## Supported filters
* `eq`: equals and this default filter
* `gt`: greater than.
* `gte`: greater than or equal.
* `lt`: less than
* `lte`: less than or equal
* `in`: the value in a list of a tuple.
* e.g. age__in=[10,20,30]
* `contains`: contains a substring as in the example.
* `icontains`: case-insensitive `contains`.
* `startswith`: checks if a value starts with a query strings.
* `istartswith`: case-insensitive `startswith`.
* `endswith`: checks if a value ends with a query strings.
* `iendswith`: case-insensitive `endswith`.
* `isnull`: checks if the value matches any of NULL_VALUES which are `('', '.', None, "None", "null", "NULL")`
* e.g. `filter__isnull=True` or `filter__isnull=False`
For `eq`,`gt`,`gte`,`lt`,`lte`, `in`, `contains`, `icontains`, `startswith`,`istartswith`, `endswith` and `iendswith`, you can add a `n` to negate the results. e.g `nin` which is equivalent to `not in`
## Advanced examples
This section will cover the use of `OR`, `AND` and `NOT`
### Usage of `OR`
`OR` or `__or__` takes a list of dictionaries to evaluate and returns with the first `True`.
```python
from leopards import Q
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"}]
filtered = Q(l, {"OR": [{"name__contains": "k"}, {"age__gte": 21}]})
print(list(filtered))
```
output
```python
[{'name': 'Mike', 'age': '19'}, {'name': 'Sarah', 'age': '21'}]
```
### Usage of `NOT`
`NOT` or `__not__` takes a dict for query run.
```python
from leopards import Q
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"}]
filtered = Q(l, {"age__gt": 15, "NOT": {"age__eq": 19}})
print(list(filtered))
```
output
```python
[{'name': 'John', 'age': '16'}, {'name': 'Sarah', 'age': '21'}]
```
### Usage of `AND`
`AND` or `__and__` takes a list of dict for query run, returns with the first `False`.
```python
from leopards import Q
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"}]
filtered = Q(l, {"__and__": [{"age__gte": 15}, {"age__lt": 21}]})
print(list(filtered))
```
output
```python
[{'name': 'John', 'age': '16'}, {'name': 'Mike', 'age': '19'}]
```
## Aggregating Data
You can run the following aggregations
* Count
* Max
* Min
* Sum
* Avg
### Count
Find the count of certain aggregated column
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Count
count = Count(l,['age'])
```
output
```python
[{"age":"16","count":1},{"age":"19","count":2}, {"age":"21","count":1}]
```
### Max
Find the Max value for a certain column in certain aggregated columns
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Max
count = Max(l,"age",['name'],dtype=int)
```
output
```python
[{'name': 'John', 'age': '19'}, {'name': 'Mike', 'age': '19'}, {'name': 'Sarah', 'age': '21'}]
```
**Notes:**
* If you don't pass the aggregation columns, the maximum will be found across dataset.
* You can pass the datatype of the column to convert it on the fly while evaluating
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Max
m = Max(l,"age",dtype=int)
```
output
```python
[{'age': 21}]
```
### Min
Find the Max value for a certain column in certain aggregated columns
```python
l = [{"name": "John", "age": "16"}, {"name": "Mike", "age": "19"}, {"name": "Sarah", "age": "21"},{"name":"John","age":"19"}]
from leopards import Min
m = Min(l,"age",['name'])
```
output
```python
[{'name': 'John', 'age': '16'}, {'name': 'Mike', 'age': '19'}, {'name': 'Sarah', 'age': '21'}]
```
**Note:**
* If you don't pass the aggregation columns, the min will be found across dataset.
* You can pass the datatype of the column to convert it on the fly while evaluating
## Sum and Avg
Like Min and Max but only works with integers and floats.
## Comparison with Pandas
This is done on Python 3.8 running on Ubuntu 22.04 on i7 11th generation and 32 GB of RAM.
| Comparison | Pandas | Leopards |
|-------------------------------------------------------------|----------|-------------|
| Package Size <br/> (Lower is better) | 29.8 MB | **7.5 KB** |
| import Time (Worst) <br/> (Lower is better) | 146 ms | **1.05 ms** |
| load 10k CSV lines<br/> (Lower is better) <sup>[1]</sup> | 0.295s | **0.138s** |
| get first matched record<br/> (Lower is better) | 0.310s | **0.017s** |
| print all filtered records (10/10k) <br/> (Lower is better) | 0.310s | **0.137s** |
| filter by integers <br/>(Lower is better) | 0.316s | **0.138s** |
<sup>[1]</sup> This was loading the whole csv in memory which was for sake of fair comparison.
Nevertheless, Leopards can work with DictReader as an iterable which executes in **0.014s**, then it handles line by line.
Thanks for [Asma Tahir](https://github.com/tahirasma) for Pandas stats.
## Contributors
* [saeedesmaili](https://github.com/saeedesmaili)
## Tutorials
* [Usage with different file types](https://dev.to/mkalioby/leopards-with-different-file-types-1d3)
* [Work on CSV Files with Leopards](https://dev.to/mkalioby/working-with-csv-by-leopards-5bmd)
================================================
FILE: USAGE.md
================================================
# Usage
This document covers how to use leopards with different file types
## CSV
`DictReader` from `csv` module can be used to read csv files as dictionaries as shown below.
```python
import csv
from leopards import Q
data = csv.DictReader(open("data.csv"))
res = Q(data, {"age__gt": 15})
```
## TSV
`DictReader` from `csv` module can be used to read tsv files as dictionaries as shown below.
```python
import csv
from leopards import Q
data = csv.DictReader(open("data.csv"), delimiter="\t")
res = Q(data, {"age__gt": 15})
```
## JSON
`json.load` can be used to read json files as dictionaries as shown below.
```python
import json
from leopards import Q
data = json.load(open("data.json"))
res = Q(data, {"age__gt": 15})
```
## XLS
`xlrd` library can be used to read xls files as dictionaries as shown below.
```python
import xlrd
from leopards import Q
wb = xlrd.open_workbook("data.xls")
sh = wb.sheets()[0]
keys = sh.row_values(0)
data =[]
for n in range(1, sh.nrows):
data.append({key: sh.row_values(n)[n2] for n2, key in enumerate(keys)})
res = Q(data, {"age__gt": 15})
```
## ClickHouse
'clickhouse_driver' library can be used to read data from ClickHouse as dictionaries as shown below.
```python
import clickhouse_connect
client = clickhouse_connect.get_client( host='localhost', username='default',password='' )
rows = client.execute("SELECT * FROM TABLE")
data = rows.named_results()
res = Q(data, {"age__gt": 15})
```
## MySQL
`mysql-client` library can be used to read data from MySQL as dictionaries as shown below.
```python
import MySQLdb
from MySQLdb.cursors import DictCursor
from leopards import Q
db=MySQLdb.connect(user='root',password='PASS', database="db", cursorclass=DictCursor)
cursor = db.cursor()
cursor.execute("SELECT * FROM TABLE")
data = cursor.fetchall()
res = Q(data, {"age__gt": 15})
```
================================================
FILE: leopards/Q.py
================================================
NULL_VALUES = ('', '.', None, "None", "null", "NULL")
def get_key_op(key:str):
"""Separate at the key to name and op"""
if "__" in key:
cols = key.split("__")
k = cols[0]
op = cols[1]
else:
k = key
op = "eq"
return k, op
def convert_value(v:str, value_type:type):
"""Converts str to the data type of the value"""
from decimal import Decimal
if value_type is float:
return float(v)
elif value_type is int:
return int(v)
elif value_type is bytes:
return bytes(v, 'ascii')
elif value_type is Decimal:
return Decimal(v)
return str(v)
def evaluate(value:type, op:str, qv:type):
"""Evaluate the current value again the query value based on type."""
if op == "eq":
return value == qv
elif op == "gt":
return value > qv
elif op == "gte":
return value >= qv
elif op == "lt":
return value < qv
elif op == "lte":
return value <= qv
elif op == "in":
return value in qv
elif op == "contains":
return qv in value
elif op == "icontains":
return qv.lower() in value.lower()
elif op == "startswith":
return value.startswith(qv)
elif op == "istartswith":
return value.lower().startswith(qv.lower())
elif op == "endswith":
return value.endswith(qv)
elif op == "iendswith":
return value.lower().endswith(qv.lower())
elif op == "isnull":
res = value in NULL_VALUES
return res == qv
def check(value:type, op:str, qv:type):
"""Checks for negation"""
if op.startswith("n"):
op = op[1:]
return not evaluate(value, op, qv)
return evaluate(value, op, qv)
================================================
FILE: leopards/Query.py
================================================
from functools import partial
try:
from Q import get_key_op, convert_value,check
except ModuleNotFoundError: # pragma: no cover
from .Q import get_key_op, convert_value,check # pragma: no cover
def Q(iterable:list, query:dict=None, convert_types=True, **kwargs):
"""
Query a list of dictionary or objects by a query dict.
:param iterable: the iterable of dicts
:param query: dictionary holding your query
:param convert_types: try to convert the field in data to match query type
:return: Iterable of type filter
"""
def filter_list(item, **kwargs):
if type(item) in (str, int, float, list, tuple):
raise TypeError("The item in the list shall be dict or object")
if type(item) is not dict:
item= item.__dict__
for k,v in kwargs.items():
if k in("OR" , "__or__"):
for q in v:
if filter_list(item,**q):
return True
return False
elif k in("AND","__and__"):
for q in v:
if not filter_list(item,**q):
return False
return True
elif k in ("NOT", "__not__"):
return not filter_list(item,**v)
key, op = get_key_op(k)
value = item[key]
if convert_types and type(v) != type(value):
value = convert_value(value, type(v))
if not check(value, op, v):
return False
return True
if query is None: query={}
query.update(kwargs)
p = partial(filter_list, **query)
return filter(p, iterable)
def Count(iterable:list, cols:list=None, col_name:str='count'):
"""
:param iterable: iterable to count
:param cols: columns used to aggregated
:param col_name: the name of count column, default: count
:return: iterable of dicts
"""
new_dict={}
for item in iterable:
if type(item) is not dict:
item=item.__dict__ # pragma: no cover
if cols:
d={k:v for k,v in item.items() if k in cols}
k = ":".join(d.values())
else:
k="ALL"
d={}
if not k in new_dict:
new_dict[k]=d
new_dict[k][col_name]=0
new_dict[k][col_name]+=1
return new_dict.values()
def Max(iterable:list, col_name:str, cols:list=None, dtype=str):
"""
:param iterable: iterable to loop through
:param col_name: the name of the column that Max shall be computed against
:param cols: columns to aggregate on
:return: iterable of dicts
"""
new_dict={}
for item in iterable:
if type(item) is not dict:
item=item.__dict__ # pragma: no cover
if cols:
d={k:v for k,v in item.items() if k in cols}
k = ":".join(d.values())
else:
k='ALL'
d={}
v: str = item[col_name]
if dtype!=str:
v = dtype(v)
if not k in new_dict:
new_dict[k]=d
new_dict[k][col_name]=v
if v>new_dict[k][col_name]:
new_dict[k][col_name] = v
return new_dict.values()
def Min(iterable:list, col_name:str, cols:list=None, dtype=str):
"""
:param iterable: iterable to loop through
:param col_name: the name of the column that Min shall be computed against
:param cols: columns to aggregate on
:param dtype: data type of the cols
:return: iterable of dicts
"""
new_dict={}
for item in iterable:
if type(item) is not dict:
item=item.__dict__ # pragma: no cover
if cols:
d={k:v for k,v in item.items() if k in cols}
k = ":".join(d.values())
else:
k="ALL"
d={}
v:str=item[col_name]
if v != str:
v=dtype(v)
if not k in new_dict:
new_dict[k]=d
new_dict[k][col_name]=v
if v<new_dict[k][col_name]:
new_dict[k][col_name] = v
return new_dict.values()
def Sum(iterable:list, col_name:str, cols:list=None):
"""
:param iterable: iterable to loop through
:param col_name: the name of the column that Sum shall be computed against
:param cols: columns to aggregate on
:return: iterable of dicts
"""
new_dict={}
for item in iterable:
if type(item) is not dict:
item=item.__dict__ # pragma: no cover
if cols:
d={k:v for k,v in item.items() if k in cols}
k = ":".join(d.values())
else:
k="ALL"
d={}
if not k in new_dict:
new_dict[k]=d
new_dict[k][col_name]=float(0)
new_dict[k][col_name] += float(item[col_name])
return new_dict.values()
def Avg(iterable:list, col_name:str, cols:list=None):
"""
:param iterable: iterable to loop through
:param col_name: the name of the column that Average shall be computed against
:param cols: columns to aggregate on
:return: iterable of dicts
"""
new_dict={}
for item in iterable:
if type(item) is not dict:
item=item.__dict__ # pragma: no cover
if cols:
d={k:v for k,v in item.items() if k in cols}
k = ":".join(d.values())
else:
k='ALL'
d={}
if not k in new_dict:
new_dict[k]=d
new_dict[k][col_name+"__sum"]=float(0)
new_dict[k][col_name+"__count"]=float(0)
new_dict[k][col_name+"__sum"] += float(item[col_name])
new_dict[k][col_name+"__count"] += 1
res = []
for d,v in new_dict.items():
v[col_name] = v.pop(col_name+"__sum")/ v.pop(col_name+"__count")
res.append(v)
return res
================================================
FILE: leopards/__init__.py
================================================
from .Query import *
================================================
FILE: setup.py
================================================
#!/usr/bin/env python
from setuptools import find_packages, setup
setup(
name='leopards',
version='1.0.0',
description='Allows filtering & aggregation iterable of dictionary by another dictionary. Much faster than pandas',
long_description=open("README.md").read(),
long_description_content_type="text/markdown",
#long_description="Filters List of Dictionaries by a query dictionary",
author='Mohamed El-Kalioby',
author_email = 'mkalioby@mkalioby.com',
url = 'https://github.com/mkalioby/leopards/',
download_url='https://github.com/mkalioby/leopards/',
license='MIT',
packages=find_packages(),
install_requires=[
],
python_requires=">=3.7",
include_package_data=True,
zip_safe=False, # because we're including static files
classifiers=[
#"Development Status :: 4 - Beta",
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development :: Libraries :: Python Modules",
]
)
================================================
FILE: tests/__init__.py
================================================
================================================
FILE: tests/test_file.py
================================================
l=[{"chromosome": "chr1", "start pos": "113460677", "end pos": "113460684", "reference": "AATAAATA", "observed": "-", "quality": "116.74", "filter": "PASS", "zygosity": "hom", "refGene function": "splicing", "refGene gene": "SLC16A1", "refGene splice info": "NM_001166496:exon4:r.spl", "refGene exonic function": "", "refGene AA change": "", "refGene exonic;splicing Info": "NM_001166496:exon4:113459799-113460666:877:+11;NM_003051:exon4:113459799-113460666:877:+11"}, {"chromosome": "chr1", "start pos": "114372214", "end pos": "114372214", "reference": "C", "observed": "G", "quality": "48.75", "filter": "PASS", "zygosity": "het", "refGene function": "exonic;splicing", "refGene gene": "PTPN22;PTPN22", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "PTPN22:NM_012411:exon16:c.2085G>C:p.Lys695Asn,PTPN22:NM_001308297:exon17:c.2178G>C:p.Lys726Asn,PTPN22:NM_001193431:exon18:c.2166G>C:p.Lys722Asn,PTPN22:NM_015967:exon18:c.2250G>C:p.Lys750Asn", "refGene exonic;splicing Info": "NM_012411:exon16:114372213-114372329:0:115;NM_015967:exon18:114372213-114372329:0:115;NM_001308297:exon17:114372213-114372329:0:115;NM_001193431:exon18:114372213-114372329:0:115"}, {"chromosome": "chr1", "start pos": "145360608", "end pos": "145360608", "reference": "T", "observed": "A", "quality": "52.12", "filter": "PASS", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "NBPF19", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "NBPF19:NM_001351365:exon69:c.8414T>A:p.Leu2805Gln", "refGene exonic;splicing Info": ""}, {"chromosome": "chr1", "start pos": "145360700", "end pos": "145360700", "reference": "C", "observed": "T", "quality": "9.32", "filter": "MG_SNP_Filter", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "NBPF19", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "NBPF19:NM_001351365:exon69:c.8506C>T:p.Arg2836Cys", "refGene exonic;splicing Info": ""}, {"chromosome": "chr10", "start pos": "125780762", "end pos": "125780762", "reference": "-", "observed": "GGGC", "quality": "159.95", "filter": ".", "zygosity": "hom", "refGene function": "exonic;splicing", "refGene gene": "CHST15;CHST15", "refGene splice info": "NM_015892:exon6:c.1347+10->GCCC;NM_001270764:exon6:c.1347+10->GCCC", "refGene exonic function": "frameshift insertion", "refGene AA change": "CHST15:NM_001270765:exon6:c.1356_1357insGCCC:p.Pro453fs,CHST15:NM_014863:exon6:c.1356_1357insGCCC:p.Pro453fs", "refGene exonic;splicing Info": "NM_015892:exon6:125780771-125780928:-10:166;NM_001270765:exon6:125779168-125780928:1593:166;NM_014863:exon6:125779168-125780928:1593:166;NM_001270764:exon6:125780771-125780928:-10:166"}, {"chromosome": "chr10", "start pos": "126714752", "end pos": "126714752", "reference": "G", "observed": "A", "quality": "140.3", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "CTBP2", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "CTBP2:NM_022802:exon1:c.1577C>T:p.Pro526Leu", "refGene exonic;splicing Info": ""}, {"chromosome": "chr11", "start pos": "12316344", "end pos": "12316344", "reference": "-", "observed": "CTCCTCCTCCTC", "quality": "68.98", "filter": "PASS", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "MICALCL", "refGene splice info": "", "refGene exonic function": "nonframeshift insertion", "refGene AA change": "MICALCL:NM_032867:exon3:c.1366_1367insCTCCTCCTCCTC:p.Ala456delinsAlaProProProPro", "refGene exonic;splicing Info": ""}, {"chromosome": "chr11", "start pos": "60899366", "end pos": "60899366", "reference": "G", "observed": "T", "quality": "7.79", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "VPS37C", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "VPS37C:NM_017966:exon5:c.994C>A:p.Pro332Thr", "refGene exonic;splicing Info": ""}, {"chromosome": "chr11", "start pos": "60899396", "end pos": "60899396", "reference": "C", "observed": "T", "quality": "7.79", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "VPS37C", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "VPS37C:NM_017966:exon5:c.964G>A:p.Gly322Ser", "refGene exonic;splicing Info": ""}, {"chromosome": "chr12", "start pos": "21375307", "end pos": "21375309", "reference": "AAA", "observed": "-", "quality": "63.71", "filter": ".", "zygosity": "hom", "refGene function": "splicing", "refGene gene": "SLCO1B1", "refGene splice info": "NM_006446:exon13:r.spl", "refGene exonic function": "", "refGene AA change": "", "refGene exonic;splicing Info": "NM_006446:exon13:21375233-21375298:73:+9"}, {"chromosome": "chr13", "start pos": "32972752", "end pos": "32972752", "reference": "T", "observed": "C", "quality": "245.66", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "BRCA2", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "BRCA2:NM_000059:exon27:c.10102T>C:p.Ser3368Pro", "refGene exonic;splicing Info": ""}, {"chromosome": "chr19", "start pos": "43865321", "end pos": "43865321", "reference": "T", "observed": "A", "quality": "17.17", "filter": "PASS", "zygosity": "het", "refGene function": "exonic", "refGene gene": "CD177", "refGene splice info": "", "refGene exonic function": "unknown", "refGene AA change": "UNKNOWN", "refGene exonic;splicing Info": ""}, {"chromosome": "chr20", "start pos": "10389422", "end pos": "10389422", "reference": "T", "observed": "C", "quality": "49.79", "filter": "PASS", "zygosity": "het", "refGene function": "exonic", "refGene gene": "MKKS", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "MKKS:NM_018848:exon4:c.1015A>G:p.Ile339Val,MKKS:NM_170784:exon4:c.1015A>G:p.Ile339Val", "refGene exonic;splicing Info": ""}, {"chromosome": "chr20", "start pos": "31672703", "end pos": "31672703", "reference": "G", "observed": "A", "quality": "175.97", "filter": ".", "zygosity": "hom", "refGene function": "exonic;splicing", "refGene gene": "BPIFB4;BPIFB4", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "BPIFB4:NM_182519:exon4:c.683G>A:p.Arg228His", "refGene exonic;splicing Info": "NM_182519:exon4:31672697-31672802:5:99"}, {"chromosome": "chr22", "start pos": "29885581", "end pos": "29885604", "reference": "AGGCCAAGTCCCCAGAGAAGGAAG", "observed": "-", "quality": "32.62", "filter": "PASS", "zygosity": "het", "refGene function": "exonic", "refGene gene": "NEFH", "refGene splice info": "", "refGene exonic function": "nonframeshift deletion", "refGene AA change": "NEFH:NM_021076:exon4:c.1952_1975del:p.651_659del", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "7811643", "end pos": "7811643", "reference": "C", "observed": "A", "quality": "118.3", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "VCX", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "VCX:NM_013452:exon3:c.207C>A:p.Ser69Arg", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "38145640", "end pos": "38145640", "reference": "C", "observed": "T", "quality": "7.77", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "RPGR", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "RPGR:NM_001034853:exon15:c.2612G>A:p.Gly871Glu", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "66765158", "end pos": "66765158", "reference": "-", "observed": "GCA", "quality": "73.91", "filter": "PASS", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "AR", "refGene splice info": "", "refGene exonic function": "nonframeshift insertion", "refGene AA change": "AR:NM_000044:exon1:c.170_171insGCA:p.Leu57delinsLeuGln,AR:NM_001348061:exon1:c.170_171insGCA:p.Leu57delinsLeuGln,AR:NM_001348063:exon1:c.170_171insGCA:p.Leu57delinsLeuGln,AR:NM_001348064:exon1:c.170_171insGCA:p.Leu57delinsLeuGln", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "71379704", "end pos": "71379704", "reference": "T", "observed": "C", "quality": "80.11", "filter": ".", "zygosity": "hom", "refGene function": "exonic;splicing", "refGene gene": "FLJ44635;FLJ44635", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "FLJ44635:NM_207422:exon2:c.25T>C:p.Tyr9His", "refGene exonic;splicing Info": "NM_207422:exon2:71379686-71381600:17:1896"}, {"chromosome": "chrX", "start pos": "100749053", "end pos": "100749053", "reference": "-", "observed": "GACTGA", "quality": "34.48", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "ARMCX4", "refGene splice info": "", "refGene exonic function": "nonframeshift insertion", "refGene AA change": "ARMCX4:NM_001256155:exon2:c.5477_5478insGACTGA:p.Gly1826delinsGlyThrGlu", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "100749065", "end pos": "100749065", "reference": "A", "observed": "G", "quality": "44.99", "filter": "PASS", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "ARMCX4", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "ARMCX4:NM_001256155:exon2:c.5489A>G:p.Glu1830Gly", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "101573515", "end pos": "101573515", "reference": "C", "observed": "T", "quality": "6.99", "filter": ".", "zygosity": "hom", "refGene function": "splicing", "refGene gene": "NXF2", "refGene splice info": "NM_022053:exon9:c.724+14C>T", "refGene exonic function": "", "refGene AA change": "", "refGene exonic;splicing Info": "NM_022053:exon9:101573431-101573501:83:+14"}, {"chromosome": "chrX", "start pos": "103349504", "end pos": "103349504", "reference": "C", "observed": "T", "quality": "115.48", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "SLC25A53", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "SLC25A53:NM_001012755:exon2:c.437G>A:p.Arg146His", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "111000973", "end pos": "111000973", "reference": "C", "observed": "T", "quality": "77.11", "filter": ".", "zygosity": "hom", "refGene function": "exonic;splicing", "refGene gene": "ALG13;ALG13", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "ALG13:NM_001324293:exon23:c.2408C>T:p.Ala803Val,ALG13:NM_001257230:exon25:c.2582C>T:p.Ala861Val,ALG13:NM_001257234:exon25:c.2582C>T:p.Ala861Val,ALG13:NM_001257237:exon25:c.2582C>T:p.Ala861Val,ALG13:NM_001324292:exon25:c.2894C>T:p.Ala965Val,ALG13:NM_001099922:exon26:c.3131C>T:p.Ala1044Val,ALG13:NM_001257231:exon26:c.2897C>T:p.Ala966Val", "refGene exonic;splicing Info": "NM_001099922:exon26:111000815-111000990:157:17;NM_001257230:exon25:111000815-111000990:157:17;NM_001324292:exon25:111000815-111000990:157:17;NM_001257231:exon26:111000815-111000990:157:17;NM_001257234:exon25:111000815-111000990:157:17;NM_001257237:exon25:111000815-111000990:157:17;NM_001324293:exon23:111000815-111000990:157:17"}, {"chromosome": "chrX", "start pos": "118985711", "end pos": "118985711", "reference": "-", "observed": "TTTTTTT", "quality": "93.71", "filter": "PASS", "zygosity": "hom", "refGene function": "splicing", "refGene gene": "UPF3B", "refGene splice info": "NM_023010:exon2:c.263+19->AAAAAAA", "refGene exonic function": "", "refGene AA change": "", "refGene exonic;splicing Info": "NM_023010:exon2:118985729-118985836:-19:125;NM_080632:exon2:118985729-118985836:-19:125"}, {"chromosome": "chrX", "start pos": "128896018", "end pos": "128896018", "reference": "C", "observed": "G", "quality": "133.37", "filter": "PASS", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "XPNPEP2", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "XPNPEP2:NM_003399:exon18:c.1640C>G:p.Ala547Gly", "refGene exonic;splicing Info": ""}, {"chromosome": "chrX", "start pos": "135429119", "end pos": "135429119", "reference": "T", "observed": "G", "quality": "142.39", "filter": ".", "zygosity": "hom", "refGene function": "exonic", "refGene gene": "ADGRG4", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "ADGRG4:NM_153834:exon6:c.3254T>G:p.Ile1085Ser", "refGene exonic;splicing Info": ""}, {"chromosome": "chrY", "start pos": "9175204", "end pos": "9175204", "reference": "T", "observed": "G", "quality": "7.77", "filter": ".", "zygosity": "hom", "refGene function": "exonic;splicing", "refGene gene": "TSPY1;TSPY10", "refGene splice info": "", "refGene exonic function": "nonsynonymous SNV", "refGene AA change": "TSPY10:NM_001282469:exon1:c.86T>G:p.Leu29Trp,TSPY10:NM_001320962:exon1:c.86T>G:p.Leu29Trp,TSPY1:NM_001320964:exon1:c.86T>G:p.Leu29Trp", "refGene exonic;splicing Info": "NM_001320962:exon1:9175072-9175337:131:133;NM_001282469:exon1:9175072-9175337:131:133;NM_001320964:exon1:9175072-9175205:131:1"}]
================================================
FILE: tests/test_leopards.py
================================================
#! /usr/bin/env python
import unittest
from decimal import Decimal
try:
from .test_file import *
except ModuleNotFoundError:
from test_file import *
def Q(data,query=None,convert_types=True,**kwargs):
from leopards.Query import Q
return list(Q(data,query,convert_types,**kwargs))
class Employee:
def __init__(self,name,age):
self.name = name
self.age = age
employees = [Employee('Ahmed',12),Employee('Mohamed',24)]
class TestConditions(unittest.TestCase):
def test_eq(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos': '114372214', 'end pos': '114372214'})), 1)
def test_neq(self):
self.assertEqual(len(Q(l, chromosome__neq='chr1')), 24)
def test_neq2(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos__neq': '114372214'})), 3)
def test_gt(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos__gt': '114372214', 'end pos__gt': '114372214'})), 2)
def test_gte(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos__gte': '114372214', 'end pos__gte': '114372214'})),
3)
def test_lt(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos__lt': '114372214'})), 1)
def test_lte(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'end pos__lte': '114372214'})), 2)
def test_in(self):
self.assertEqual(len(Q(l, chromosome__in=('chr1', 'chr22'))), 5)
def test_nin(self):
self.assertEqual(len(Q(l, chromosome__nin=('chr1', 'chr22'))), 23)
def test_contains(self):
self.assertEqual(len(Q(l, chromosome__contains='chr2')), 3)
def test_ncontains(self):
self.assertEqual(len(Q(l, chromosome__ncontains='chr1')), 16)
def test_contains2(self):
self.assertEqual(len(Q(l, chromosome__contains='Chr2')), 0)
def test_ncontains2(self):
self.assertEqual(len(Q(l, chromosome__ncontains='Chr2')), 28)
def test_icontains(self):
self.assertEqual(len(Q(l, chromosome__icontains='Chr2')), 3)
def test_icontains2(self):
self.assertEqual(len(Q(l, chromosome__icontains='chr2')), 3)
def test_nicontains(self):
self.assertEqual(len(Q(l, chromosome__nicontains='Chr2')), 25)
def test_nicontains2(self):
self.assertEqual(len(Q(l, chromosome__nicontains='chr2')), 25)
def test_null(self):
self.assertEqual(len(Q(l, filter__isnull=True)), 16)
def test_null2(self):
self.assertEqual(len(Q(l, {"filter__isnull": False})), 12)
def test_startswith(self):
self.assertEqual(len(Q(l, {"reference__startswith": 'T'})), 7)
def test_istartswith(self):
self.assertEqual(len(Q(l, {"reference__istartswith": 't'})), 7)
def test_nstartswith(self):
self.assertEqual(len(Q(l, {"reference__nstartswith": 'T'})), 21)
def test_endswith(self):
self.assertEqual(len(Q(l, {"observed__endswith": 'C'})), 5)
def test_iendsswith(self):
self.assertEqual(len(Q(l, {"observed__iendswith": 'C'})), 5)
def test_nendswith(self):
self.assertEqual(len(Q(l, {"observed__nendswith": 'C'})), 23)
class TestConverations(unittest.TestCase):
def test_int(self):
self.assertEqual(len(Q(l, {"chromosome": 'chr1', 'start pos': 114372214, 'end pos': 114372214})), 1)
def test_bytes(self):
self.assertEqual(len(Q(l, {"chromosome": b'chr1', 'start pos': 114372214, 'end pos': 114372214})), 1)
def test_float(self):
res = Q(l, {"quality__gte": 133.47, "quality__lt": 142.39})
self.assertEqual(len(res), 1)
def test_decimal(self):
res = Q(l, {"quality__gte": Decimal(133.47), "quality__lte": Decimal(142.39)})
self.assertEqual(len(res), 1)
class TestCombination(unittest.TestCase):
def test_or(self):
res = Q(l, {"OR": [{"chromosome": 'chr1', 'start pos': 114372214, 'end pos': 114372214},
{"reference": 'C', 'observed': 'A'}]})
self.assertEqual(len(res), 2)
def test_and(self):
res = Q(l, {"__and__": [{"chromosome": 'chr1', 'start pos': 114372214, 'end pos': 114372214},
{"reference__neq": 'G'}]})
self.assertEqual(len(res), 1)
def test_not(self):
self.assertEqual(len(Q(l, {"reference": "T", "NOT": {"observed": "C"}})), 4)
class TestObjects(unittest.TestCase):
def test_obj(self):
self.assertEqual(len(Q(employees,age__gt=13)),1)
class TestException(unittest.TestCase):
def testInt(self):
self.assertRaises(TypeError, Q, [1,2,3],{"i__gt":1})
class TestAgg(unittest.TestCase):
def testCountDefault(self):
from leopards import Count
res = Count(l, ["chromosome"])
self.assertEqual(list(Q(res, chromosome="chr20"))[0]["count"], 2)
def testCountDefault2(self):
from leopards import Count
res = Count(l)
self.assertEqual(list(res)[0]["count"], 28)
def testCountCol(self):
from leopards import Count
res= Count(l,["chromosome"],"chr_count")
self.assertEqual(list(Q(res,chromosome="chr1"))[0]["chr_count"],4)
def testCountCol2(self):
from leopards import Count
res= Count(l,col_name="chr_count")
self.assertEqual(list(res)[0]['chr_count'],28)
def testMax(self):
from leopards import Max
res = Max(l, "start pos", ["chromosome"])
self.assertEqual(list(Q(res, chromosome="chr10"))[0]["start pos"], "126714752")
def testMax2(self):
from leopards import Max
res = Max(l, "quality",dtype=float)
self.assertEqual(list(res)[0]['quality'], 245.66)
def testMin(self):
from leopards import Min
res = Min(l, "end pos", ["chromosome"],dtype=float)
self.assertEqual(list(Q(res, chromosome="chrX"))[0]["end pos"], 7811643)
def testMin2(self):
from leopards import Min
res = Min(l, "quality", dtype=float)
self.assertEqual(list(res)[0]["quality"], 6.99)
def testSum(self):
from leopards import Sum
res = Sum(l, "quality", ["chromosome"])
v="%.2f" % list(Q(res, chromosome="chr11"))[0]["quality"]
self.assertEqual(v, "84.56")
def testSum2(self):
from leopards import Sum
res = Sum(l, "quality")
v="%.2f" % list(res)[0]["quality"]
self.assertEqual(v, "2133.04")
def testAvg(self):
from leopards import Avg
res = Avg(l, "quality", ["chromosome"])
v="%.2f" % list(Q(res, chromosome="chr11"))[0]["quality"]
self.assertEqual(v, "28.19")
def testAvg2(self):
from leopards import Avg
res = Avg(l, "quality")
v="%.2f" % list(res)[0]["quality"]
self.assertEqual(v, "76.18")
if __name__ == '__main__':
unittest.main()
================================================
FILE: tox.ini
================================================
[tox]
envlist=
py37,
py38,
py39
py310
py311
py312
py313
[testenv]
deps =
allowlist_externals =
pytest
commands =
pytest --cov=leopards tests/ --cov-report term-missing
gitextract_8g1d786o/ ├── .github/ │ └── workflows/ │ └── workflow.yml ├── .gitignore ├── LICENSE ├── README.md ├── USAGE.md ├── leopards/ │ ├── Q.py │ ├── Query.py │ └── __init__.py ├── setup.py ├── tests/ │ ├── __init__.py │ ├── test_file.py │ └── test_leopards.py └── tox.ini
SYMBOL INDEX (65 symbols across 3 files)
FILE: leopards/Q.py
function get_key_op (line 4) | def get_key_op(key:str):
function convert_value (line 16) | def convert_value(v:str, value_type:type):
function evaluate (line 30) | def evaluate(value:type, op:str, qv:type):
function check (line 61) | def check(value:type, op:str, qv:type):
FILE: leopards/Query.py
function Q (line 7) | def Q(iterable:list, query:dict=None, convert_types=True, **kwargs):
function Count (line 45) | def Count(iterable:list, cols:list=None, col_name:str='count'):
function Max (line 71) | def Max(iterable:list, col_name:str, cols:list=None, dtype=str):
function Min (line 101) | def Min(iterable:list, col_name:str, cols:list=None, dtype=str):
function Sum (line 132) | def Sum(iterable:list, col_name:str, cols:list=None):
function Avg (line 158) | def Avg(iterable:list, col_name:str, cols:list=None):
FILE: tests/test_leopards.py
function Q (line 10) | def Q(data,query=None,convert_types=True,**kwargs):
class Employee (line 14) | class Employee:
method __init__ (line 15) | def __init__(self,name,age):
class TestConditions (line 20) | class TestConditions(unittest.TestCase):
method test_eq (line 21) | def test_eq(self):
method test_neq (line 24) | def test_neq(self):
method test_neq2 (line 27) | def test_neq2(self):
method test_gt (line 30) | def test_gt(self):
method test_gte (line 33) | def test_gte(self):
method test_lt (line 37) | def test_lt(self):
method test_lte (line 40) | def test_lte(self):
method test_in (line 43) | def test_in(self):
method test_nin (line 46) | def test_nin(self):
method test_contains (line 49) | def test_contains(self):
method test_ncontains (line 52) | def test_ncontains(self):
method test_contains2 (line 55) | def test_contains2(self):
method test_ncontains2 (line 58) | def test_ncontains2(self):
method test_icontains (line 61) | def test_icontains(self):
method test_icontains2 (line 64) | def test_icontains2(self):
method test_nicontains (line 67) | def test_nicontains(self):
method test_nicontains2 (line 70) | def test_nicontains2(self):
method test_null (line 73) | def test_null(self):
method test_null2 (line 76) | def test_null2(self):
method test_startswith (line 79) | def test_startswith(self):
method test_istartswith (line 82) | def test_istartswith(self):
method test_nstartswith (line 85) | def test_nstartswith(self):
method test_endswith (line 88) | def test_endswith(self):
method test_iendsswith (line 91) | def test_iendsswith(self):
method test_nendswith (line 94) | def test_nendswith(self):
class TestConverations (line 97) | class TestConverations(unittest.TestCase):
method test_int (line 98) | def test_int(self):
method test_bytes (line 101) | def test_bytes(self):
method test_float (line 104) | def test_float(self):
method test_decimal (line 108) | def test_decimal(self):
class TestCombination (line 113) | class TestCombination(unittest.TestCase):
method test_or (line 114) | def test_or(self):
method test_and (line 120) | def test_and(self):
method test_not (line 125) | def test_not(self):
class TestObjects (line 129) | class TestObjects(unittest.TestCase):
method test_obj (line 130) | def test_obj(self):
class TestException (line 133) | class TestException(unittest.TestCase):
method testInt (line 134) | def testInt(self):
class TestAgg (line 137) | class TestAgg(unittest.TestCase):
method testCountDefault (line 138) | def testCountDefault(self):
method testCountDefault2 (line 142) | def testCountDefault2(self):
method testCountCol (line 146) | def testCountCol(self):
method testCountCol2 (line 151) | def testCountCol2(self):
method testMax (line 156) | def testMax(self):
method testMax2 (line 161) | def testMax2(self):
method testMin (line 166) | def testMin(self):
method testMin2 (line 171) | def testMin2(self):
method testSum (line 177) | def testSum(self):
method testSum2 (line 183) | def testSum2(self):
method testAvg (line 189) | def testAvg(self):
method testAvg2 (line 195) | def testAvg2(self):
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (46K chars).
[
{
"path": ".github/workflows/workflow.yml",
"chars": 954,
"preview": "name: test\n\non: [push]\n\nenv:\n MODE: Test\njobs:\n tests:\n name: \"Python ${{ matrix.python-version }} on ${{ matrix.os"
},
{
"path": ".gitignore",
"chars": 1811,
"preview": "env/\n.idea/\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distributi"
},
{
"path": "LICENSE",
"chars": 1075,
"preview": "MIT License\n\nCopyright (c) 2022 Mohamed El-Kalioby\n\nPermission is hereby granted, free of charge, to any person obtainin"
},
{
"path": "README.md",
"chars": 7028,
"preview": "# Leopards\n\n[](https://badge.fury.io/py/leopards)\n[![Python Versio"
},
{
"path": "USAGE.md",
"chars": 1867,
"preview": "# Usage \n\nThis document covers how to use leopards with different file types\n\n## CSV\n\n`DictReader` from `csv` module can"
},
{
"path": "leopards/Q.py",
"chars": 1749,
"preview": "NULL_VALUES = ('', '.', None, \"None\", \"null\", \"NULL\")\n\n\ndef get_key_op(key:str):\n \"\"\"Separate at the key to name and "
},
{
"path": "leopards/Query.py",
"chars": 5836,
"preview": "from functools import partial\ntry:\n from Q import get_key_op, convert_value,check\nexcept ModuleNotFoundError: # prag"
},
{
"path": "leopards/__init__.py",
"chars": 20,
"preview": "from .Query import *"
},
{
"path": "setup.py",
"chars": 1515,
"preview": "#!/usr/bin/env python\n\nfrom setuptools import find_packages, setup\n\nsetup(\n name='leopards',\n version='1.0.0',\n "
},
{
"path": "tests/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tests/test_file.py",
"chars": 13306,
"preview": "l=[{\"chromosome\": \"chr1\", \"start pos\": \"113460677\", \"end pos\": \"113460684\", \"reference\": \"AATAAATA\", \"observed\": \"-\", \"q"
},
{
"path": "tests/test_leopards.py",
"chars": 6871,
"preview": "#! /usr/bin/env python\nimport unittest\nfrom decimal import Decimal\n\ntry:\n from .test_file import *\nexcept ModuleNotFo"
},
{
"path": "tox.ini",
"chars": 208,
"preview": "[tox]\nenvlist=\n py37,\n py38,\n py39\n py310\n py311\n py312\n py313\n\n\n[testenv]\ndeps =\n\nallowlist_extern"
}
]
About this extraction
This page contains the full source code of the mkalioby/leopards GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (41.2 KB), approximately 13.3k tokens, and a symbol index with 65 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.