%global _empty_manifest_terminate_build 0
Name:		python-mngs
Version:	0.4.0
Release:	1
Summary:	For lazy python users (monogusa people in Japanse), especially in ML/DSP fields
License:	GPL3.0
URL:		https://github.com/ywatanabe1989/mngs
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f2/95/0bc28b15ac436c49c9becb10049eddf3c0c94860908fbe74e94ca3d20afc/mngs-0.4.0.tar.gz
BuildArch:	noarch

Requires:	python3-chardet
Requires:	python3-GitPython
Requires:	python3-h5py
Requires:	python3-joblib
Requires:	python3-matplotlib
Requires:	python3-natsort
Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-pymatreader
Requires:	python3-PyYAML
Requires:	python3-scipy
Requires:	python3-seaborn
Requires:	python3-sklearn
Requires:	python3-statsmodels
Requires:	python3-torch
Requires:	python3-xmltodict
Requires:	python3-openpyxl
Requires:	python3-obspy
Requires:	python3-pyro-ppl
Requires:	python3-torchaudio
Requires:	python3-scikit-learn
Requires:	python3-psutil
Requires:	python3-pyedflib

%description
## Requirements
```
chardet
GitPython
h5py
joblib
matplotlib
natsort
numpy
pandas
pymatreader
PyYAML
scipy
seaborn
sklearn
statsmodels
torch
xmltodict
```

## Installation
``` bash
$ pip install mngs

or

$ pip install git+https://github.com/ywatanabe1989/mngs.git@develop
```



## mngs.general.save
``` python
import mngs
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

## numpy
arr = np.arange(10)
mngs.general.save(arr, 'spath.npy')

## pandas
df = pd.DataFrame(arr)
mngs.general.save(df, 'spath.csv')

## matplotlib
fig, ax = plt.subplots()
ax.plot(arr)
mngs.general.save(fig, 'spath.png)
```

## mngs.general.load
``` python
import mngs
arr = mngs.general.load('spath.npy')
arr = mngs.general.load('spath.mat')
df = mngs.general.load('spath.npy')
yaml_dict = mngs.general.load('spath.yaml')
hdf5_dict = mngs.general.load('spath.hdf5')
```

## mngs.general.fix_seeds

``` python
import mngs
import os
import random
import numpy as np
import torch

mngs.general.fix_seeds(os=os, random=random, np=np, torch=torch, tf=None, seed=42)
```

## mngs.general.tee
``` python
import sys
sys.stdout, sys.stderr = tee(sys)
print("abc")  # also wrriten in stdout
print(1 / 0)  # also wrriten in stderr
```

## mngs.plt.configure_mpl
``` python
configure_mpl(
    plt,
    dpi=100,
    figsize=(16.2, 10),
    figscale=1.0,
    fontsize=16,
    labelsize="same",
    legendfontsize="xx-small",
    tick_size="auto",
    tick_width="auto",
    hide_spines=False,
)
```

## mngs.plt.ax_*
- mngs.plt.ax_extend
- mngs.plt.ax_scientific_notation
- mngs.plt.ax_set_position

## mngs.ml.Reporter
Now, classification task is available.
``` python
reporter = mngs.ml.Reporter(sdir=log_dir)
for i_fold in range(N_FOLDS):
    ...
    print("\n--- Metrics ---\n")
    reporter.calc_metrics(
        T_tes,
        pred_class_tes,
        pred_proba_tes,
        labels=[class_0, class_1, class_2],
        i_fold=i_fold,
    )
    print("\n---------------\n")

reporter.summarize()
reporter.save()
```

The above lines makes reportes and figures.
``` bash
$ tree $log_dir
├── aucs.csv
├── bACCs.csv
├── balanced_accs.csv
├── clf_reports.csv
├── conf_mat
│   ├── conf_mats.csv
│   ├── fold#0.png
│   ├── fold#1.png
│   ├── fold#2.png
│   └── overall_sum.png
├── mccs.csv
├── pre_rec_curves
│   ├── fold#0.png
│   ├── fold#1.png
│   └── fold#2.png
└── roc_curves
    ├── fold#0.png
    ├── fold#1.png
    └── fold#2.png
```




%package -n python3-mngs
Summary:	For lazy python users (monogusa people in Japanse), especially in ML/DSP fields
Provides:	python-mngs
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-mngs
## Requirements
```
chardet
GitPython
h5py
joblib
matplotlib
natsort
numpy
pandas
pymatreader
PyYAML
scipy
seaborn
sklearn
statsmodels
torch
xmltodict
```

## Installation
``` bash
$ pip install mngs

or

$ pip install git+https://github.com/ywatanabe1989/mngs.git@develop
```



## mngs.general.save
``` python
import mngs
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

## numpy
arr = np.arange(10)
mngs.general.save(arr, 'spath.npy')

## pandas
df = pd.DataFrame(arr)
mngs.general.save(df, 'spath.csv')

## matplotlib
fig, ax = plt.subplots()
ax.plot(arr)
mngs.general.save(fig, 'spath.png)
```

## mngs.general.load
``` python
import mngs
arr = mngs.general.load('spath.npy')
arr = mngs.general.load('spath.mat')
df = mngs.general.load('spath.npy')
yaml_dict = mngs.general.load('spath.yaml')
hdf5_dict = mngs.general.load('spath.hdf5')
```

## mngs.general.fix_seeds

``` python
import mngs
import os
import random
import numpy as np
import torch

mngs.general.fix_seeds(os=os, random=random, np=np, torch=torch, tf=None, seed=42)
```

## mngs.general.tee
``` python
import sys
sys.stdout, sys.stderr = tee(sys)
print("abc")  # also wrriten in stdout
print(1 / 0)  # also wrriten in stderr
```

## mngs.plt.configure_mpl
``` python
configure_mpl(
    plt,
    dpi=100,
    figsize=(16.2, 10),
    figscale=1.0,
    fontsize=16,
    labelsize="same",
    legendfontsize="xx-small",
    tick_size="auto",
    tick_width="auto",
    hide_spines=False,
)
```

## mngs.plt.ax_*
- mngs.plt.ax_extend
- mngs.plt.ax_scientific_notation
- mngs.plt.ax_set_position

## mngs.ml.Reporter
Now, classification task is available.
``` python
reporter = mngs.ml.Reporter(sdir=log_dir)
for i_fold in range(N_FOLDS):
    ...
    print("\n--- Metrics ---\n")
    reporter.calc_metrics(
        T_tes,
        pred_class_tes,
        pred_proba_tes,
        labels=[class_0, class_1, class_2],
        i_fold=i_fold,
    )
    print("\n---------------\n")

reporter.summarize()
reporter.save()
```

The above lines makes reportes and figures.
``` bash
$ tree $log_dir
├── aucs.csv
├── bACCs.csv
├── balanced_accs.csv
├── clf_reports.csv
├── conf_mat
│   ├── conf_mats.csv
│   ├── fold#0.png
│   ├── fold#1.png
│   ├── fold#2.png
│   └── overall_sum.png
├── mccs.csv
├── pre_rec_curves
│   ├── fold#0.png
│   ├── fold#1.png
│   └── fold#2.png
└── roc_curves
    ├── fold#0.png
    ├── fold#1.png
    └── fold#2.png
```




%package help
Summary:	Development documents and examples for mngs
Provides:	python3-mngs-doc
%description help
## Requirements
```
chardet
GitPython
h5py
joblib
matplotlib
natsort
numpy
pandas
pymatreader
PyYAML
scipy
seaborn
sklearn
statsmodels
torch
xmltodict
```

## Installation
``` bash
$ pip install mngs

or

$ pip install git+https://github.com/ywatanabe1989/mngs.git@develop
```



## mngs.general.save
``` python
import mngs
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

## numpy
arr = np.arange(10)
mngs.general.save(arr, 'spath.npy')

## pandas
df = pd.DataFrame(arr)
mngs.general.save(df, 'spath.csv')

## matplotlib
fig, ax = plt.subplots()
ax.plot(arr)
mngs.general.save(fig, 'spath.png)
```

## mngs.general.load
``` python
import mngs
arr = mngs.general.load('spath.npy')
arr = mngs.general.load('spath.mat')
df = mngs.general.load('spath.npy')
yaml_dict = mngs.general.load('spath.yaml')
hdf5_dict = mngs.general.load('spath.hdf5')
```

## mngs.general.fix_seeds

``` python
import mngs
import os
import random
import numpy as np
import torch

mngs.general.fix_seeds(os=os, random=random, np=np, torch=torch, tf=None, seed=42)
```

## mngs.general.tee
``` python
import sys
sys.stdout, sys.stderr = tee(sys)
print("abc")  # also wrriten in stdout
print(1 / 0)  # also wrriten in stderr
```

## mngs.plt.configure_mpl
``` python
configure_mpl(
    plt,
    dpi=100,
    figsize=(16.2, 10),
    figscale=1.0,
    fontsize=16,
    labelsize="same",
    legendfontsize="xx-small",
    tick_size="auto",
    tick_width="auto",
    hide_spines=False,
)
```

## mngs.plt.ax_*
- mngs.plt.ax_extend
- mngs.plt.ax_scientific_notation
- mngs.plt.ax_set_position

## mngs.ml.Reporter
Now, classification task is available.
``` python
reporter = mngs.ml.Reporter(sdir=log_dir)
for i_fold in range(N_FOLDS):
    ...
    print("\n--- Metrics ---\n")
    reporter.calc_metrics(
        T_tes,
        pred_class_tes,
        pred_proba_tes,
        labels=[class_0, class_1, class_2],
        i_fold=i_fold,
    )
    print("\n---------------\n")

reporter.summarize()
reporter.save()
```

The above lines makes reportes and figures.
``` bash
$ tree $log_dir
├── aucs.csv
├── bACCs.csv
├── balanced_accs.csv
├── clf_reports.csv
├── conf_mat
│   ├── conf_mats.csv
│   ├── fold#0.png
│   ├── fold#1.png
│   ├── fold#2.png
│   └── overall_sum.png
├── mccs.csv
├── pre_rec_curves
│   ├── fold#0.png
│   ├── fold#1.png
│   └── fold#2.png
└── roc_curves
    ├── fold#0.png
    ├── fold#1.png
    └── fold#2.png
```




%prep
%autosetup -n mngs-0.4.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-mngs -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.0-1
- Package Spec generated