zabop

[tool.poetry]
name = "reprod"
version = "0.1.0"
description = ""
authors = ["zabop <[email protected]>"]
readme = "README.md"

[tool.poetry.dependencies]
python = "3.11.9"
mapproxy = {git = "https://github.com/mapproxy/mapproxy.git"}

zabop

# docker build . --tag proxytest
# docker run --interactive --tty --publish 8080:8080 proxytest

FROM python:3.11.9

RUN pip3 install poetry
COPY pyproject.toml .
RUN poetry install

COPY mapproxy.yaml .

zabop

import shapely.geometry
import shapely.affinity
import geopandas as gpd

origin = shapely.geometry.Point(67596.000000, 36694.000000)
pixel_count = 256
units_per_pixel_for_each_zoom_level = [
    352.77758727788068426889,
    176.38879363894034213445,
    88.19439681947017106722,

zabop

zabop

zabop

dfs = pd.read_html('https://en.wikipedia.org/wiki/Doppler_spectroscopy')

zabop

import pandas as pd
df = pd.read_html('https://en.wikipedia.org/wiki/List_of_cities_in_India_by_population')[0]

zabop

plt.figure(figsize=(8,6))
plt.plot(epochs, history_dict1['val_acc'], 'b', linewidth=5, label='Accuracy, ReLU, 16 neurons')
plt.plot(epochs, history_dict2['val_acc'], 'r', linewidth=5, label='Accuracy, Sigmoid, 16 neurons')
plt.plot(epochs, history_dict3['val_acc'], 'c', linewidth=5, label='Accuracy, ReLU, 64 neurons')
plt.plot(epochs, history_dict4['val_acc'], 'k', linewidth=5, label='Accuracy, Sigmoid, 64 neurons')

plt.title('Accuracy vs epochs',fontsize=22)
plt.xlabel('Epochs',fontsize=18)
plt.ylabel('Accuracy',fontsize=18)

zabop

plt.figure(figsize=(8,6))

epochs = range(1, 21)

plt.plot(epochs, val_loss_values1, 'b', linewidth=5, label='Validation loss, ReLU, 16 neurons')
plt.plot(epochs, val_loss_values2, 'r', linewidth=5, label='Validation loss, Sigmoid, 16 neurons')
plt.plot(epochs, val_loss_values3, 'c', linewidth=5, label='Validation loss, ReLU, 64 neurons')
plt.plot(epochs, val_loss_values4, 'k', linewidth=5, label='Validation loss, Sigmoid, 64 neurons')

plt.title('Training and validation loss',fontsize=22)

zabop

history_dict1 = history1.history
loss_values1 = history_dict1['loss']
val_loss_values1 = history_dict1['val_loss']