semvec/pypi-packages huggingface dataset:
azcam-status 21.2.1
azcam browser-based status page
Keywords: ccd imaging astronomy observation observatory

# azcam-status

*azcam-status* is an *azcam* extension which implements a browser-based status page.

## Installation

`pip install azcam-status`

Or download from github: https://github.com/mplesser/azcam-status.git.

## Usage

Open a web browser to http://localhost:2403/status with the appropriate replacements for localhost and the web server port number.
azcam-qhy 21.2.1
azcam extension for QHY cameras
Keywords: python parameters

# azcam-qhy

*azcam-qhy* is an *azcam* extension for QHY cameras. See https://www.qhyccd.com/.

This code has been used for the QHY174 and QHY600 cameras.

## Installation

`pip install azcam-qhy`

Or download from github: https://github.com/mplesser/azcam-qhy.git.

## Example Code

The code below is for example only.

### Controller

```python
import azcam.server
from azcam_qhy.controller_qhy174 import ControllerQHY
controller = ControllerQHY()
```

### Exposure

```python
import azcam.server
from azcam_qhy.exposure_qhy174 import ExposureQHY
exposure = ExposureQHY()
filetype = "FITS"
exposure.filetype = exposure.filetypes[filetype]
exposure.image.filetype = exposure.filetypes[filetype]
exposure.display_image = 1
exposure.image.remote_imageserver_flag = 0
exposure.set_filename("/data/QHY/image.fits")
exposure.display_image = 1
```
hipo-map 1.4.8
Histopathological image analysis using Grad-CAM representation map
Requires Python: >=3

# HipoMap

![](Capture.PNG)

## Installation
HipoMap support `Python 3`.
It requires `numpy`, `pandas`, `tensorflow`, `scipy`, `scikit-learn`, `seaborn`, `matplotlib`, `openslide-python`, `cv2`.

Quick installation of openslide
1. Update system
```
sudo apt-get update
```
2. install openslide-tools
```
sudo apt-get install openslide-tools
```
3. install openslide
```
pip install openslide
```

```
pip install hipo_map
```

## Documentation

## Quick Start
```python
#Model load
#if you want to loaded keras pre-trained model
from tensorflow.keras.applications.vgg16 import VGG16
model = VGG16()

#if you want to loaded your pre-trained model
from tensorflow.keras.models import load_model 
model = model = load_model(r'./pre_model.h5')

#make representation map
from hipo_map.Hipomap import generateHipoMap
generateHipoMap(inputpath="/home/user/Dataset/", outputpath="/home/user/Rep/", model = model, layer_name="block5_conv3", patch_size=(224, 224))

#draw heatmap
from hipo_map.Hipomap import  draw_represent
draw_represent(path="/home/yeon/Dataset/", K=50, max_value=1000)

#Classify data to cancer/normal with representation map
from hipo_map.hipoClassify import HipoMap
hipomap = HipoMap(K=50)

#1. split data with base(.csv) 
trainset, validset, testset = hipomap.split("./split.csv", dir_normal="/home/user/Dataset/Normal/", dir_cancer="/home/user/Dataset/Cancer")

#2. train the classifier
hipo_model = hipomap.fit(trainset, validset, lr=0.1, epoch=20, batchsize=1, activation_size=196)

#3. get prediction value
prediction = hipomap.predict(test_X=testset[0])

#4. get score (tpr, fpr, auc)
tpr, mean_fpr, auc = hipomap.evaluate_score(label=testset[1])
```