水文模型的通量和存储图
在Python中绘制具有变量节点（股票）大小以及箭头（流量）的Sankey图表的最简单方法是什么？
我目前正在研究水文模型。模型的结构可以在下面看到
在模型的每个时间步长处，不同存储之间存在通量，因此存储减少了存储水量的增加。为了更好地可视化我想要绘制它们的通量和存储变化，使用Python，因为它是我所知道的唯一语言。抽取的存储器和助焊剂的尺寸应根据存储器中包含的或通过助焊剂传输的水量而变化。它或多或少应该看起来像模型结构，但随着尺寸的变化。
起初我考虑使用matplotlib的Sankey图，但这证明是不够的，因为它只绘制通量而不是存储。我的第二个想法是使用来自matplotlib的圆圈和箭头自己编写代码，但这将是相当多的工作，因为我只是开始编程它也可能看起来很难看。
所以我的问题是： 有没有一个Python工具可以实现绘制通量并存储我想象的方式或者是否有另一种方式在python中自己做这个？
1 个答案:
答案 0 :(得分：3)
编辑：现在可以在github找到：https://github.com/zutn/fluxogram
所以我玩了一下，最后得到了一个有效的解决方案，比我预期的更通用，更粗糙。输出看起来像这样：
编辑：现在它也可以作为动画使用，可以看到here
每个存储都由一个矩形表示，每个通量都是一个箭头。用法类似于sankey图之一。我在这里添加了代码，如果有人有同样的问题，可能会有一些使用它。如何使用Fluxogram的一个例子是在帖子的最后。
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 22 14:13:32 2016
@author: Florian Jehn
"""
import numpy as np
import os
import subprocess
from matplotlib import pyplot as plt
from matplotlib import animation
class Fluxogram:
"""
a class to draw and maintain all fluxes and storages from a model or
some similiar kind of thing to be drawn as a sequence of storages
and fluxes. Storages and fluxes are not drawn proportional
it should look something like this:
order offset=-1 center offset=1
----------------------------------------------------
1. . . . stor1 . . .
. . .arrow . arrow . arrow . .
2. .stor2 . . stor3 . . stor4 .
. . . . . . arrow .
3. . . . . . stor5 .
"""
def __init__(self, max_flux, max_storage, grid_size = 20, storages = None,
fluxes = None):
"""
initilalizes a fluxogram. must be called with:
- max_flux: aximum flux of all fluxes; needed for scaling
- max_storage: maximum storages of all storages; needed for scaling
- grid_size:grid_size for drawing the fluxogram, determines how big
everything is. Fluxes and storages scaled accordingly
- storages: all the storages the fluxogram has (usually empy to
begin with)
- fluxes: all the fluxes the fluxogram has (usually empty to begin
with)
"""
if storages == None:
self.storages = []
if fluxes == None:
self.fluxes = []
self.max_flux = max_flux
self.max_storage = max_storage
self.grid_size = grid_size
def add_storage(self, name, amount, order, offset):
"""
add a storage to the storages of the fluxogram
"""
# len(self.storages is used to give the storages consecutive numbers)
self.storages.append(Storage(name, self.grid_size,len(self.storages) ,
amount, order, offset))
def add_flux(self, name, from_storage, to_storage, amount):
"""
add a flux to the fluxes of the fluxogram
"""
self.fluxes.append(Flux(name, self.grid_size, from_storage, to_storage,
amount))
def update_all_storages(self, amounts):
"""
updates the amount of all storages
"""
for storage, amount in zip(self.storages, amounts):
storage.update_storage(amount)
def update_all_fluxes(self, amounts):
"""
updates the amount of all fluxes
"""
for flux, amount in zip(self.fluxes, amounts):
flux.update_flux(amount)
def update_everything(self, amounts_storages, amounts_fluxes):
"""
updates all fluxes and storages
"""
self.update_all_fluxes(amounts_fluxes)
self.update_all_storages(amounts_storages)
def draw(self, day = -1):
"""
draws all fluxes and storages
"""
plt.axes()
# find the smallest/largest offset_ so the fluxogram can be drawn big
# enough
largest_offset = 0
smallest_offset = 0
largest_order = 0
for storage in self.storages:
if storage.offset > largest_offset:
largest_offset = storage.offset
if storage.offset < smallest_offset:
smallest_offset = storage.offset
if storage.order > largest_order:
largest_order = storage.order
# set y and x limits
y_max = 0
y_min = (largest_order + 1) * 2 * self.grid_size * -1
x_max = (largest_offset + 2) * 2 * self.grid_size
x_min = (smallest_offset - 1) * 2 * self.grid_size
plt.axis([x_min, x_max, y_min, y_max])
# draw all fluxes
for flux in self.fluxes:
# scale the amount
scaled_amount_flux = self.scaler(flux.amount, self.max_flux)
# width multiplied because if not, the arrows are so tiny
arrow = plt.Arrow(flux.x_start, flux.y_start, flux.dx, flux.dy,
width = scaled_amount_flux * 1.7, alpha = 0.8)
plt.gca().add_patch(arrow)
# draw all storages
for storage in self.storages:
# scale the amount
scaled_amount_stor = self.scaler(storage.amount, self.max_storage)
if scaled_amount_stor == 0:
scaled_amount_stor = 0.0001
# change_x and y, so the storages are centered to the middle
# of their position and not to upper left
x = (storage.x + (1 - storage.amount / self.max_storage) * 0.5
* self.grid_size)
y = (storage.y - (1 - storage.amount / self.max_storage) * 0.5
* self.grid_size)
rectangle = plt.Rectangle((x, y), scaled_amount_stor,
-scaled_amount_stor, alpha = 0.4)
# label all storages
plt.text(storage.x + self.grid_size * 0.1,
storage.y - self.grid_size * 1.2, storage.name,
fontsize = self.grid_size * 0.7)
# draw a date
if day > -1:
plt.text(x_min + 0.5 * self.grid_size,
y_min + 0.5 * self.grid_size,
"Day: " + str(day))
plt.gca().add_patch(rectangle)
def show(self):
"""
shows the current fluxogram on screen
"""
plt.show()
def animate(self, timeseries_fluxes, timeseries_storages, anim_name):
"""
animates the shit out of a timeseries
flux and storage timeseries need to be equally long
must be called with:
- timeseries_fluxes: a timeseries with amounts for all fluxes for
every day of the timeseries
- timeseries_storages: a timeseries with amounts for all storages
for every day of the timeseries
- anim_name: the name the animation should have
"""
# test if both time series are equally long
if len(timeseries_fluxes) != len(timeseries_storages):
print("Timeseries are not equally long, abort")
return
# draw all seperate fluxogram for all days and save them in working
# directory
print("Start making the single frames")
for day in range(len(timeseries_fluxes)):
self.update_everything(timeseries_storages[day],
timeseries_fluxes[day])
self.draw(day)
file_name = "_temp%05d.png" % day
plt.savefig(file_name, dpi = 150)
plt.clf()
day += 1
# tells the user that the program isn't crashed
if day % 100 == 0:
print("Working...please wait")
# change directory to the current working directoy
cwd = os.getcwd()
os.chdir(cwd)
# try to delete videos with the same name as the one that is to be
# made, as ffmpeg doesn't overwrite old ones. shell = True is needed
# as only with this standart command line arguments can be used
try:
subprocess.call("del " + anim_name + ".mpg", shell = True)
except FileNotFoundError:
print("No video with same name --> proceeding")
print("Start making animation")
name = anim_name + ".mpg"
# calls the command line and in there ffmpeg to stich all pictures
# together to one video
subprocess.check_call(["ffmpeg","-r", "20", "-i", "_temp%05d.png",
name])
print("Finished --> cleaning up")
# delete all the temporary pictures
subprocess.call("del _temp*.png", shell = True)
print("All done")
def scaler(self, value_in, base_max):
"""
scales the fluxes and storages, so they don't overstep their grafical
bounds must be called with:
- valueIn: the value that needs rescaling
- baseMax: the upper limit of the original dataset
~ 100 for fluxes, ~250 for stores (in my model)
"""
# baseMin: the lower limit of the original dataset (usually zero)
base_min = 0
# limitMin: the lower limit of the rescaled dataset (usually zero)
limit_min = 0
# limitMax: the upper limit of the rescaled dataset (in our case grid)
limit_max = self.grid_size
# prevents wrong use of scaler
if value_in > base_max:
raise ValueError("Input value larger than base max")
return (((limit_max - limit_min) * (value_in - base_min)
/ (base_max - base_min)) + limit_min)
class Flux:
"""
a flux of a fluxogram
"""
def __init__(self, name, grid_size, from_storage, to_storage, amount = 0):
"""
initializes a flux with:
- name: name of the flux
- grid_size: grid size of the diagram
- from_storage: storage the flux is originating from
- to_storage: storage the flux is going into
- amount: how much stuff fluxes
"""
self.name = name
self.from_storage = from_storage
self.to_storage = to_storage
self.amount = amount
self.grid_size = grid_size
self.x_start,self.y_start,self.x_end,self.y_end, self.dx, self.dy = (
self.calc_start_end_dx_dy())
def update_flux(self, amount):
"""
update the amount of the flux
"""
self.amount = amount
def calc_start_end_dx_dy(self):
"""
calculates the starting and ending point of an arrow depending on the
order and offset of the starting and ending storages. This helps
determine the direction of the arrow
returns the start and end xy coordinates of the arrow as tuples
"""
# small corrections of x_start/y_start are to make a little gap
# between the arrow and the storage
correction_factor = 0.25
# arrow pointing to left up
if (self.from_storage.offset > self.to_storage.offset and
self.from_storage.order > self.to_storage.order):
x_start = self.from_storage.x - self.grid_size * correction_factor
y_start = self.from_storage.y + self.grid_size * correction_factor
x_end = self.to_storage.x + self.grid_size
y_end = self.to_storage.y - self.grid_size
dx = abs(x_start - x_end) * (-1)
dy = abs(y_start - y_end)
# arrow pointing up
elif (self.from_storage.offset == self.to_storage.offset and
self.from_storage.order > self.to_storage.order):
x_start = self.from_storage.x + 0.5 * self.grid_size
y_start = self.from_storage.y + self.grid_size * correction_factor
x_end = self.to_storage.x + 0.5 * self.grid_size
y_end = self.to_storage.y - self.grid_size
dx = abs(x_start - x_end)
dy = abs(y_start - y_end)
# arrow pointing right up
elif (self.from_storage.offset < self.to_storage.offset and
self.from_storage.order > self.to_storage.order):
x_start = (self.from_storage.x + self.grid_size + self.grid_size *
correction_factor)
y_start = self.from_storage.y + self.grid_size * correction_factor
x_end = self.to_storage.x
y_end = self.to_storage.y - self.grid_size
dx = abs(x_start - x_end)
dy = abs(y_start - y_end)
# arrow pointing right
elif (self.from_storage.offset < self.to_storage.offset and
self.from_storage.order == self.to_storage.order):
x_start = (self.from_storage.x + self.grid_size + self.grid_size *
correction_factor)
y_start = self.from_storage.y - 0.5 * self.grid_size
x_end = self.to_storage.x
y_end = self.to_storage.y - 0.5 * self.grid_size
dx = abs(x_start - x_end)
dy = abs(y_start - y_end)
# arrow pointing right down
elif (self.from_storage.offset < self.to_storage.offset and
self.from_storage.order < self.to_storage.order):
x_start = (self.from_storage.x + self.grid_size + self.grid_size *
correction_factor)
y_start = (self.from_storage.y - self.grid_size - self.grid_size *
correction_factor)
x_end = self.to_storage.x
y_end = self.to_storage.y
dx = abs(x_start - x_end)
dy = abs(y_start - y_end) * (-1)
# arrow pointing down
elif (self.from_storage.offset == self.to_storage.offset and
self.from_storage.order < self.to_storage.order):
x_start = self.from_storage.x + 0.5 * self.grid_size
y_start = (self.from_storage.y - self.grid_size - self.grid_size *
correction_factor)
x_end = self.to_storage.x + 0.5 * self.grid_size
y_end = self.to_storage.y
dx = abs(x_start - x_end)
dy = abs(y_start - y_end) * (-1)
# arrow pointing left down
elif (self.from_storage.offset > self.to_storage.offset and
self.from_storage.order < self.to_storage.order):
x_start = self.from_storage.x - self.grid_size * correction_factor
y_start = (self.from_storage.y - self.grid_size - self.grid_size *
correction_factor)
x_end = self.to_storage.x + self.grid_size
y_end = self.to_storage.y
dx = abs(x_start - x_end) * (-1)
dy = abs(y_start - y_end) * (-1)
# arrow pointing left
elif (self.from_storage.offset > self.to_storage.offset and
self.from_storage.order == self.to_storage.order):
x_start = self.from_storage.x - self.grid_size * correction_factor
y_start = self.from_storage.y - 0.5 *self.grid_size
x_end = self.to_storage.x + self.grid_size
y_end = self.to_storage.y - 0.5 * self.grid_size
dx = abs(x_start - x_end) * (-1)
dy = abs(y_start - y_end)
# multiply by 0.9 so there is a gap between storages and arrows
dx = dx * 0.75
dy = dy * 0.75
return x_start, y_start, x_end, y_end, dx, dy
class Storage:
"""
a storage of a fluxogram
"""
def __init__(self, name, grid_size, number, amount = 0, order = 0,
offset = 0):
"""initializes a storage with:
- name: name of the storage
- number: consecutive number
- grid_size of the diagram
- amount: how much stuff is in it
- order: how much down it is in the hierachie (starts with 0)
- offset = how much the storage is offset to the left/right
in relationship to the center
"""
self.name = name
self.amount = amount
self.number = number
self.order = order
self.offset = offset
self.grid_size = grid_size
self.x, self.y = self.calculate_xy()
def update_storage(self, amount):
"""
update the amount of the storage
"""
self.amount = amount
def calculate_xy(self):
"""
calculates the xy coordinates of the starting point from where
the recangle is drawn. The additional multiplication by two is
to produce the gaps in the diagram
"""
x = self.offset * self.grid_size * 2
# multiply by -1 to draw the diagram from top to bottom
y = self.order * self.grid_size * 2 * -1
return x,y
如何使用它：
"""
Example of usage
"""
# make a fluxgram instance
fl = Fluxogram(100, 150, grid_size = 10)
# add storages
fl.add_storage("up", 23, 0, 0)
fl.add_storage("right", 130, 1, 1)
fl.add_storage("middle", 149, 1, 0)
fl.add_storage("right_up", 90, 0, 2)
fl.add_storage("right_down", 50, 2, 1)
fl.add_storage("down", 23, 2, 0)
fl.add_storage("left_down", 76, 2, -1)
fl.add_storage("left", 43, 1, -2)
fl.add_storage("left_up", 34, 0, -1)
fl.add_storage("down_down", 78, 3, 0)
# add fluxes
fl.add_flux("middle_to_up", fl.storages[2], fl.storages[0], 30)
fl.add_flux("middle_to_right_up", fl.storages[2], fl.storages[3], 50)
fl.add_flux("middle_to_right", fl.storages[2], fl.storages[1], 100)
fl.add_flux("middle_to_right_down", fl.storages[2], fl.storages[4], 35)
fl.add_flux("middle_to_down", fl.storages[2], fl.storages[5], 50)
fl.add_flux("middle_to_left_down", fl.storages[2], fl.storages[6], 10)
fl.add_flux("middle_to_left", fl.storages[2], fl.storages[7], 50)
fl.add_flux("middle_to_left_up", fl.storages[2], fl.storages[8], 25)
fl.add_flux("down_to_down_down", fl.storages[5], fl.storages[9], 25)
fl.add_flux("down_to_left_down", fl.storages[5], fl.storages[4], 25)
# draw
fl.draw()
# show the fluxgram
fl.show()
# generate data for updating the plots and
data_fluxes = []
data_storages = []
for i in range(len(fl.storages) + len(fl.fluxes)):
if i % 2 == 0:
data_fluxes.append(np.random.randint(0, fl.max_flux))
else:
data_storages.append(np.random.randint(0, fl.max_storage))
# update fluxes/storages
fl.update_everything(data_storages, data_fluxes)
# draw again
fl.draw()
fl.show()
# generate random somehow oscillating data for animation
timeseries = {"fluxes" : {}, "storages" : {}}
timespan_timeseries = 150
timeseries["fluxes"][0] = data_fluxes
timeseries["storages"][0] = data_storages
for day in range(1, timespan_timeseries):
timeseries["fluxes"][day] = []
for flux in range(len(timeseries["fluxes"][0])):
upper_limit = timeseries["fluxes"][day - 1][flux] + 5
lower_limit = timeseries["fluxes"][day - 1][flux] - 5
new_flux = np.random.randint(lower_limit, upper_limit)
timeseries["fluxes"][day].append(new_flux)
if timeseries["fluxes"][day][flux] > fl.max_flux:
timeseries["fluxes"][day][flux] = fl.max_flux
if timeseries["fluxes"][day][flux] < 0:
timeseries["fluxes"][day][flux] = 0
timeseries["storages"][day] = []
for storage in range(len(timeseries["storages"][0])):
upper_limit = timeseries["storages"][day - 1][storage] + 5
lower_limit = timeseries["storages"][day - 1][storage] - 5
new_storage = np.random.randint(lower_limit, upper_limit)
timeseries["storages"][day].append(new_storage)
if timeseries["storages"][day][storage] > fl.max_storage:
timeseries["storages"][day][storage] = fl.max_storage
if timeseries["storages"][day][storage] < 0:
timeseries["storages"][day][storage] = 0
# animate the timeseries
fl.animate(timeseries["fluxes"], timeseries["storages"],
"example_animation")
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