import copy

import json

import os.path

import tempfile

import threading

import warnings

from collections import Counter

from math import sqrt, ceil, floor

from typing import Optional

import numpy as np

class PlotMixin:

"""Helper functions for plotting the arrays. """

def summary(self):

"""Print the structure and attribute summary of this DocumentArray object.

.. warning::

Calling {meth}`.summary` on large DocumentArray can be slow.

"""

from rich.table import Table

from rich.console import Console

from rich import box

all_attrs = self._get_attributes('non_empty_fields')

attr_counter = Counter(all_attrs)

table = Table(box=box.SIMPLE, title='Documents Summary')

table.show_header = False

table.add_row('Length', str(len(self)))

is_homo = len(attr_counter) == 1

table.add_row('Homogenous Documents', str(is_homo))

all_attrs_names = set(v for k in all_attrs for v in k)

_nested_in = []

if 'chunks' in all_attrs_names:

_nested_in.append('chunks')

if 'matches' in all_attrs_names:

_nested_in.append('matches')

if _nested_in:

table.add_row('Has nested Documents in', str(tuple(_nested_in)))

if is_homo:

table.add_row('Common Attributes', str(list(attr_counter.items())[0][0]))

else:

for _a, _n in attr_counter.most_common():

if _n <= 1:

_doc_text = f'{_n} Document has'

else:

_doc_text = f'{_n} Documents have'

if len(_a) == 1:

_text = f'{_doc_text} one attribute'

elif len(_a) == 0:

_text = f'{_doc_text} no attribute'

else:

_text = f'{_doc_text} attributes'

table.add_row(_text, str(_a))

console = Console()

all_attrs_names = tuple(sorted(all_attrs_names))

if not all_attrs_names:

console.print(table)

return

attr_table = Table(box=box.SIMPLE, title='Attributes Summary')

attr_table.add_column('Attribute')

attr_table.add_column('Data type')

attr_table.add_column('#Unique values')

attr_table.add_column('Has empty value')

all_attrs_values = self._get_attributes(*all_attrs_names)

if len(all_attrs_names) == 1:

all_attrs_values = [all_attrs_values]

for _a, _a_name in zip(all_attrs_values, all_attrs_names):

try:

_a = set(_a)

except:

pass # intentional ignore as some fields are not hashable

_set_type_a = set(type(_aa).__name__ for _aa in _a)

attr_table.add_row(

_a_name,

str(tuple(_set_type_a)),

str(len(_a)),

str(any(_aa is None for _aa in _a)),

)

console.print(table, attr_table)

def plot_embeddings(

self,

title: str = 'MyDocumentArray',

path: Optional[str] = None,

image_sprites: bool = False,

min_image_size: int = 16,

channel_axis: int = -1,

start_server: bool = True,

port: Optional[int] = None,

) -> str:

"""Interactively visualize :attr:`.embeddings` using the Embedding Projector.

:param title: the title of this visualization. If you want to compare multiple embeddings at the same time,

make sure to give different names each time and set ``path`` to the same value.

:param port: if set, run the embedding-projector frontend at given port. Otherwise a random port is used.

:param image_sprites: if set, visualize the dots using :attr:`.uri` and :attr:`.blob`.

:param path: if set, then append the visualization to an existing folder, where you can compare multiple

embeddings at the same time. Make sure to use a different ``title`` each time .

:param min_image_size: only used when `image_sprites=True`. the minimum size of the image

:param channel_axis: only used when `image_sprites=True`. the axis id of the color channel, ``-1`` indicates the color channel info at the last axis

:param start_server: if set, start a HTTP server and open the frontend directly. Otherwise, you need to rely on ``return`` path and serve by yourself.

:return: the path to the embeddings visualization info.

"""

from ...helper import random_port, __resources_path__

path = path or tempfile.mkdtemp()

emb_fn = f'{title}.tsv'

meta_fn = f'{title}.metas.tsv'

config_fn = f'config.json'

sprite_fn = f'{title}.png'

if image_sprites:

img_per_row = ceil(sqrt(len(self)))

canvas_size = min(img_per_row * min_image_size, 8192)

img_size = max(int(canvas_size / img_per_row), min_image_size)

max_docs = ceil(canvas_size / img_size) ** 2

if len(self) > max_docs:

warnings.warn(

f'''

{self!r} has more than {max_docs} elements, which is the maximum number of image sprites can support.

The resulting visualization may not be correct. You can do the following:

- use fewer images: `da[:10000].plot_embeddings()`

- reduce the `min_image_size` to a smaller number, say 8 or 4 (but bear in mind you can hardly recognize anything with a 4x4 image)

- turn off `image_sprites` via `da.plot_embeddings(image_sprites=False)`

'''

)

self.plot_image_sprites(

os.path.join(path, sprite_fn),

canvas_size=canvas_size,

min_size=min_image_size,

channel_axis=channel_axis,

)

self.save_embeddings_csv(os.path.join(path, emb_fn), delimiter='\t')

_exclude_fields = ('embedding', 'blob', 'scores')

with_header = True

if len(set(self[0].non_empty_fields).difference(set(_exclude_fields))) <= 1:

with_header = False

self.save_csv(

os.path.join(path, meta_fn),

exclude_fields=_exclude_fields,

dialect='excel-tab',

with_header=with_header,

)

_epj_config = {

'embeddings': [

{

'tensorName': title,

'tensorShape': list(self.embeddings.shape),

'tensorPath': f'/static/{emb_fn}',

'metadataPath': f'/static/{meta_fn}',

'sprite': {

'imagePath': f'/static/{sprite_fn}',

'singleImageDim': (img_size,) * 2,

}

if image_sprites

else {},

}

]

}

if os.path.exists(os.path.join(path, config_fn)):

with open(os.path.join(path, config_fn)) as fp:

old_config = json.load(fp)

_epj_config['embeddings'].extend(old_config.get('embeddings', []))

with open(os.path.join(path, config_fn), 'w') as fp:

json.dump(_epj_config, fp)

import gzip

with gzip.open(

os.path.join(__resources_path__, 'embedding-projector/index.html.gz'), 'rt'

) as fr, open(os.path.join(path, 'index.html'), 'w') as fp:

fp.write(fr.read())

if start_server:

def _get_fastapi_app():

from fastapi import FastAPI

from starlette.middleware.cors import CORSMiddleware

from starlette.staticfiles import StaticFiles

app = FastAPI()

app.add_middleware(

CORSMiddleware,

allow_origins=['*'],

allow_credentials=True,

allow_methods=['*'],

allow_headers=['*'],

)

app.mount('/static', StaticFiles(directory=path), name='static')

return app

import uvicorn

app = _get_fastapi_app()

port = port or random_port()

t_m = threading.Thread(

target=uvicorn.run,

kwargs=dict(app=app, port=port, log_level='error'),

daemon=True,

)

t_m.start()

url_html_path = (

f'http://localhost:{port}/static/index.html?config={config_fn}'

)

try:

import webbrowser

webbrowser.open(url_html_path, new=2)

except:

pass # intentional pass, browser support isn't cross-platform

finally:

print(

f'You should see a webpage opened in your browser, '

f'if not, you may open {url_html_path} manually'

)

t_m.join()

return path

def plot_image_sprites(

self,

output: Optional[str] = None,

canvas_size: int = 512,

min_size: int = 16,

channel_axis: int = -1,

) -> None:

"""Generate a sprite image for all image blobs in this DocumentArray-like object.

An image sprite is a collection of images put into a single image. It is always square-sized.

Each sub-image is also square-sized and equally-sized.

:param output: Optional path to store the visualization. If not given, show in UI

:param canvas_size: the size of the canvas

:param min_size: the minimum size of the image

:param channel_axis: the axis id of the color channel, ``-1`` indicates the color channel info at the last axis

"""

if not self:

raise ValueError(f'{self!r} is empty')

import matplotlib.pyplot as plt

img_per_row = ceil(sqrt(len(self)))

img_size = int(canvas_size / img_per_row)

if img_size < min_size:

# image is too small, recompute the size

img_size = min_size

img_per_row = int(canvas_size / img_size)

max_num_img = img_per_row ** 2

sprite_img = np.zeros(

[img_size * img_per_row, img_size * img_per_row, 3], dtype='uint8'

)

img_id = 0

for d in self:

_d = copy.deepcopy(d)

if _d.content_type != 'blob':

_d.load_uri_to_image_blob()

channel_axis = -1

_d.set_image_blob_channel_axis(channel_axis, -1).set_image_blob_shape(

shape=(img_size, img_size)

)

row_id = floor(img_id / img_per_row)

col_id = img_id % img_per_row

sprite_img[

(row_id * img_size) : ((row_id + 1) * img_size),

(col_id * img_size) : ((col_id + 1) * img_size),

] = _d.blob

img_id += 1

if img_id >= max_num_img:

break

from PIL import Image

im = Image.fromarray(sprite_img)

if output:

with open(output, 'wb') as fp:

im.save(fp)

else:

plt.gca().set_axis_off()

plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)

plt.margins(0, 0)

plt.gca().xaxis.set_major_locator(plt.NullLocator())

plt.gca().yaxis.set_major_locator(plt.NullLocator())

plt.imshow(im)

plt.show()