semvec/pypi-packages huggingface dataset:
crimeanalitictools 1.1.0
Statistical methods for studying criminal activity using artificial intelligence (AI)
Requires Python: >=3.6

# CrimeAnaliticTools
Statistical methods for studying criminal activity using artificial intelligence (AI)

The package “CrimeanaliticTools” contains functions for a forensic analysis of crimes data, including their exploration analysis and preliminary preparation, some functions for simplifying the use of artificial intelligence methods.

In particular, the package contains the following functions:
Dixon, Grubbs and Irvin test to identify outliers;
computes the area under the curve (AUC), AUC variance and the confidence interval (CI) of AUC;
calculates a cross-tabulation of observed and predicted classes with associated statistics;
variable importance using Garson's and Olden's algorithm, and sensitivity analysis using Lek's profile method for input variables for a Multi-layer Perceptron classifier or regressor;
assessment of the relative importance of features in data for estimator;
search for the best combination of features in data using ensemble methods;
for to draw a Multi-layer Perceptron classifier or regressor in image.
pralab-phys 0.3.14
Add your description here
Requires Python: >=3.11

# pralab-phys 0.4.0

Pralab-phys provides some extensions of QCoDes (python-based data acquisition framework) and Plotly and more.

Especially, it includes following QCoDes Instrument Drivers

- Keithley2182A
- Keithley6221
- Yokogawa7651
- Quantum Design Instruments (PPMS, Dynacool)

### Attention: 
For versions below 0.3.11, we **strongly discourage usage** due to instability in versioning and other concerns.


# pralab-phys 0.4.0 日本語解説

## 概要
本ライブラリは、物性研究室で用いることを想定した、QCoDesをベースとした測定用ライブラリであり、これを用いることで、様々な計測器制御や測定プログラムの作成が容易に行えます。

このライブラリは、QCoDesを中心としたライブラリの拡張ドライバとして、以下の機能を提供します。

QCoDesの基本的な使い方については、[公式ドキュメント](https://microsoft.github.io/Qcodes/)またはqiita記事をご覧ください。

### 各計測器の制御用ライブラリ
- Keithley2182A Nanovoltmeter
- keithley6221 DC and AC Current Source
- Yokogawa7651 電流源
- エヌエフ LI5650 ロックインアンプ
- Quantum Design Multivu経由の装置制御（QDInstrument.dll必須）
- M81 (予定)

### より平易なリアルタイムプロット機能
- RealtimePlotモジュールによる、Jupyter上での測定結果確認


## インストール&最初の利用
本ライブラリは、uvを用いたPython仮想環境上へのインストールを推奨しています。

Windows Powershellを用いたインストール手順は以下の通りです。

1. 仮想環境を作成したいディレクトリ上で以下のコードを入力（任意の仮想環境名を入力しない場合、該当ディレクトリにそのまま仮想環境が構築されます）

```
uv init <任意の仮想環境名>
```

2. pralab-physライブラリのインストール

```
uv add pralab-phys
```

3. jupyter labを起動する。自動的にブラウザが開き、Jupyter Labが使える状態になる。

```
uv run jupyter lab
```

もしブラウザが開かない場合、Powershell上に出力されたリンク(localhost:9999など)をブラウザに入力する。

Jupyter Labを終了する場合、Powershell上でctrl+cを入力。
clustertools-2 1.0.0
Tools for cluster analysis (include Diana Clustering Algoritm and Searching for optimal count of clusters for a Data Set)
Requires Python: >=3.6

# clustertools_2
The package “clustertools_2” contains functions for cluster analysis (include Diana Clustering Algoritm, Hybrid hierarchical k-means clustering algoritm and Searching for optimal count of clusters for a Data Set).

In particular, the functions are provided for identifying in the data of the trends to clustering: for сalculate the Hopkins’ statistic and for visualize Principal component analysis (PCA).
Function 'dist' computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a dataframe.
Identification and visualize the optimal number of clusters using different methods: within cluster sums of squares, average silhouette and gap statistics. NbClust function provides 30 indices for determining the number of clusters and proposes to user the best clustering scheme from the different results obtained by varying all combinations of number of clusters, distance measures, and clustering methods.
The package also includes a functions for Heuristic Identification of Noisy Variables (HINoV) method for clustering and Identification of differences between signs of data set clusters based on the T-test.
Create linkage matrix for plot the dendrogram and plot the hierarchical clustering as a dendrogram. Also may be returned of mean within-cluster distances and mean inter-cluster distances.
Two dendrograms can be compared using coefficient of measures entanglement between two dendrograms and draw a tanglegram plot of a side by side trees.
It's finally, possible visualization of partitioning methods including K-means, K-medoids, CLARA, AGNES, DIANA. An ellipse is drawn around each cluster.