semvec/pypi-packages huggingface dataset:
plotly-calplot 0.1.20
Calendar Plot made with Plotly
License: MIT
Requires Python: >=3.8,<4.0.0

# Calendar Heatmap with Plotly
Making it easier to visualize and costumize time relevant or time series data with plotly interaction.

New to the library? Read [this Medium article](https://medium.com/@brunorosilva/5fc322125db7).

This plot is a very similar to the contribuitions available on Github and Gitlab profile pages and to [Calplot](https://github.com/tomkwok/calplot) - which is a pyplot implementation of the calendar heatmap, thus it is not interactive right off the bat.

The first mention I could find of this plot being made with plotly was in [this forum post](https://community.plotly.com/t/colored-calendar-heatmap-in-dash/10907/16) and it got my attention as something that should be easily available to anyone.

# Installation
``` bash
pip install plotly-calplot
```

# Examples

In [this Medium article](https://medium.com/@brunorosilva/5fc322125db7) I covered lot's of usage methods for this library.
``` python
from plotly_calplot import calplot

fig = calplot(df, x="date", y="value")
fig.show()
# you can also adjust layout and your usual plotly stuff
```

<img src="https://github.com/brunorosilva/plotly-calplot/blob/main/assets/images/example.png?raw=true">
m-profiling-mf 0.1.9
Mobio Profiling Management Fields
License: MIT
Requires Python: >=3
Keywords: mobio,profiling-mf

- **Thư viện fields của Profiling** :
  - 0.1.8 upgrade library marshmallow, python-dateutil, pytz, m-caching
  - 0.1.7 upgrade requests library
  - 0.1.6.4 fix normalized device_id 
  - 0.1.6.3 upgrade admin_sdk support encrypt data
  - 0.1.6.1 upgrade admin_sdk
  - 0.1.6 using admin_sdk
  - 0.1.5.5 support internal_id
  - 0.1.5.4 singleton merchant_config
  - 0.1.5.3 support validate phone_number
  - 0.1.5.2 update check unification rule
  - 0.1.5.1 fix profile_tag get update data
  - 0.1.5 fix marital_status get update data
  - 0.1.4 fix get UDT support trigger
  - 0.1.3 remove provinces data
kern-smooth 1.2.3
A python implementation of KernSmooth package (https://cran.r-project.org/web/packages/KernSmooth):kernel smoothing and density estimation functions based on the book: Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
License: MIT
Keywords: statistics,probability,KDE,PDF,kernel density estimation

KernSmooth for Python
=====================
Porting popular R library KernSmooth to python.

Functions for Kernel Smoothing and Density Estimation.

Transformed R and Fortran functions into Python(2,3) code.

Attention:
----------

Please use kern-smooth 1.1.0 or newer.
Reason: found not needed log10 density transformation.


Installation:
-------------

.. code:: shell

		pip install kern-smooth


Main function of the module:
----------------------------

.. code:: python
        
        def densCols(x, y=None, nbin=128, bandwidth=None)


Produces a vector of numbers which encode the local densities at each point in dataset.

x, y : 1D numpy array with coordinates of the points density will be estimated on

nbin : [optional] int or [int, int] - number of bins along each axis
    (in case of single value - [nbin, nbin] will be used). Default value 128.

bandwidth : [optional] numeric vector (len of 1 or 2) of smoothing bandwidth.

Returns: numpy array with numerical representation (in range [0,1]) of point densities.

Attention: For return value numpy.nan values are allowed in case of nan / infinite values in original dataset 

Source: R::grDevices::densCols


Usage
-----

Generate data for plotting (make sure Matplotlib is installed)

.. code:: python

    from matplotlib import pyplot as plt
    from matplotlib import cm
    import numpy as np
    np.random.seed(0)
    # create two 'bulbs' with normal distributions
    mean1 = [0, 0]
    cov1 = [[5, 0], [0, 30]]  # diagonal covariance
    x1, y1 = np.random.multivariate_normal(mean1, cov1, 50000).T
    mean2 = [5, 17]
    cov2 = [[30, 0], [0, 5]]  # diagonal covariance
    x2, y2 = np.random.multivariate_normal(mean2, cov2, 50000).T
    x = np.hstack([x1,x2])
    y = np.hstack([y1,y2])


Generate point densities:

.. code:: python

    from kern_smooth import densCols
    densities = densCols(x, y, nbin = 128)


Plot the result

.. code:: python

    sc = plt.scatter(x, y, c=densities, s=15, edgecolors='none', alpha=0.75, cmap=cm.jet)
    plt.colorbar(sc)
    plt.show()


Result
------
![Result](https://github.com/AlexanderButyaev/kern_smooth/blob/master/example_density.png) 

Author
------
Alexander Butyaev