Jupyter Book and GitHub repo.
Annotation#
This is a start on interactively annotating data.
%matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.autolayout"] = True
locations = []
last_d = (0, 0)
# 0 print depth and data with time, markers on ascent_start, ascent_end, descent_end,
# print: T1 local time, Midnight or Noon or Quotidian
# Options: <done> <unresolved> <critical> <previous> <continue this>
# 1 from <continue this> above
# print: max chlor, depth at max, surface chlor, depth at surface, binned depth count
# click on max locations or
# <done> goes to next profile
# <unresolved> <critical> goes to next profile
# <+floor> goes to floor state 2
# 2 floor
# click on time/depth where floor signal is reached, up to two of these
# print: depth of floor signal
# <done> <unresolved> <critical> goes to next profile
# 3 from <+lens> above
# click on groups of 3 points early-mid-late to get 3*n times/depths/values for this lens
# <Add this lens> compiles average of all triples; opens a new lens
# <Re-Do This Lens> clears data for this lens
# <Re-Do All Lenses> clears data for all lenses
# <+note> goes to note state 3
# <done> <unresolved> <critical> goes to next profile
# 4 from <+note> above
# Write a note or indicate 'of interest' with a combination of ! and ?
# <done> <unresolved> <critical> goes to next profile
#
# Pandas dataframe has columns
# status: ok, unresolved, critical
# T0
# T1
# T2
# ascent duration: td64
# descent duration: td64
# profile type: midn, noon, quot
# chlora max: value
# chlora max depth: value
# chlora surface: value
# chlora surface depth: value
# chlora floor: value
# chlora floor depth: value
# chlora n lenses: value
# chlora lens 1 thickness: value (meters)
# chlora lens 1 signal: value (max-min)
# chlora lens 1 signal norm: value (signal / chlora max)
# chlora lens 1 v1: value
# chlora lens 1 d1: value (depth)
# chlora lens 1 v2: value
# chlora lens 1 d2: value (depth)
# chlora lens 1 v3: value
# chlora lens 1 d3: value (depth)
# chlora lens 2 thickness: value (meters)
# chlora lens 2 signal: value (max-min)
# chlora lens 2 signal norm: value (signal / chlora max)
# chlora lens 2 v1: value
# chlora lens 2 d1: value (depth)
# chlora lens 2 v2: value
# chlora lens 2 d2: value (depth)
# chlora lens 2 v3: value
# chlora lens 2 d3: value (depth)
# chlora lens 3 thickness: value (meters)
# chlora lens 3 signal: value (max-min)
# chlora lens 3 signal norm: value (signal / chlora max)
# chlora lens 3 v1: value
# chlora lens 3 d1: value (depth)
# chlora lens 3 v2: value
# chlora lens 3 d2: value (depth)
# chlora lens 3 v3: value
# chlora lens 3 d3: value (depth)
# chlora lens 4 thickness: value (meters)
# chlora lens 4 signal: value (max-min)
# chlora lens 4 signal norm: value (signal / chlora max)
# chlora lens 4 v1: value
# chlora lens 4 d1: value (depth)
# chlora lens 4 v2: value
# chlora lens 4 d2: value (depth)
# chlora lens 4 v3: value
# chlora lens 4 d3: value (depth)
# note: string
annotate_state = 0
def mouse_event(event):
this_x, this_y = round(event.xdata, 2), round(event.ydata, 2)
locations.append((this_x, this_y))
# ax.plot(last_d[0], last_d[1], color='white')
last_d = (this_x, this_y)
axs[0].plot([last_d[0]], [last_d[1]], color='red', ms=12)
msg = str(this_x) + ' ' + str(this_y)
text_object = axs[1].text(0.0, .7, msg, color='k', fontsize=10)
text_object.set_bbox(dict(facecolor='white', alpha=1.0, edgecolor='b'))
fig.canvas.draw()
fig,axs = plt.subplots(2, figsize=(8,8), gridspec_kw={'height_ratios': [5,1]})
fig.canvas.mpl_connect('button_press_event', mouse_event)
x = np.linspace(-10, 10, 100)
y = np.sin(x)
axs[0].plot(x, y)
plt.show()
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/matplotlib/backends/registry.py:407, in BackendRegistry.resolve_gui_or_backend(self, gui_or_backend)
406 try:
--> 407 return self.resolve_backend(gui_or_backend)
408 except Exception: # KeyError ?
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/matplotlib/backends/registry.py:369, in BackendRegistry.resolve_backend(self, backend)
368 if gui is None:
--> 369 raise RuntimeError(f"'{backend}' is not a recognised backend name")
371 return backend, gui if gui != "headless" else None
RuntimeError: 'widget' is not a recognised backend name
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
Cell In[1], line 1
----> 1 get_ipython().run_line_magic('matplotlib', 'widget')
3 import numpy as np
4 import matplotlib.pyplot as plt
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2482, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
2480 kwargs['local_ns'] = self.get_local_scope(stack_depth)
2481 with self.builtin_trap:
-> 2482 result = fn(*args, **kwargs)
2484 # The code below prevents the output from being displayed
2485 # when using magics with decorator @output_can_be_silenced
2486 # when the last Python token in the expression is a ';'.
2487 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/IPython/core/magics/pylab.py:103, in PylabMagics.matplotlib(self, line)
98 print(
99 "Available matplotlib backends: %s"
100 % _list_matplotlib_backends_and_gui_loops()
101 )
102 else:
--> 103 gui, backend = self.shell.enable_matplotlib(args.gui)
104 self._show_matplotlib_backend(args.gui, backend)
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3667, in InteractiveShell.enable_matplotlib(self, gui)
3664 import matplotlib_inline.backend_inline
3666 from IPython.core import pylabtools as pt
-> 3667 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
3669 if gui != None:
3670 # If we have our first gui selection, store it
3671 if self.pylab_gui_select is None:
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/IPython/core/pylabtools.py:349, in find_gui_and_backend(gui, gui_select)
347 else:
348 gui = _convert_gui_to_matplotlib(gui)
--> 349 backend, gui = backend_registry.resolve_gui_or_backend(gui)
351 gui = _convert_gui_from_matplotlib(gui)
352 return gui, backend
File ~/micromamba/envs/geosmart-template/lib/python3.12/site-packages/matplotlib/backends/registry.py:409, in BackendRegistry.resolve_gui_or_backend(self, gui_or_backend)
407 return self.resolve_backend(gui_or_backend)
408 except Exception: # KeyError ?
--> 409 raise RuntimeError(
410 f"'{gui_or_backend} is not a recognised GUI loop or backend name")
RuntimeError: 'widget is not a recognised GUI loop or backend name
locations
[(-2.98, 0.63),
(1.29, 0.12),
(4.53, 0.29),
(4.43, 0.66),
(-0.92, 0.51),
(-2.88, 0.14),
(-1.36, -0.36),
(2.97, -0.44),
(7.05, -0.47),
(8.7, -0.77),
(0.47, 0.4),
(0.67, 0.56),
(0.42, 0.74),
(0.21, 0.29),
(0.05, 0.26),
(0.05, 0.56),
(0.41, 0.34)]
Discarded code#
Some line magic associated with IPython#
%env
%matplotlib --list
%matplotlib
From a CSV file select two columns for a scatter chart.#
%matplotlib inline
df = pd.read_csv('./tmp.csv')
# positions = []
# def onclick(event): positions.append(event.xdata, event.ydata)
fig,ax = plt.subplots()
ax.scatter(df['total_bill'], df['tip'])
ax.set(xlabel='Bill')
ax.set(ylabel='Tip')
# fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
Figure size as a set parameter.#
plt.rcParams["figure.figsize"] = [7.00, 3.50]
Using {} as a print argument placeholder, with .format.#
print('x: {} and y: {}'.format(this_x, this_y))
Loading an ndarray into numpy (first inflammation file from carpentries) for use with imshow()#
import numpy, matplotlib
data = numpy.loadtxt(fname='./tmp1.csv', delimiter=',')
img = matplotlib.pyplot.imshow(data)
Below: Some vector drawing.#
%matplotlib widget
import numpy as np
from math import acos, pi
import matplotlib.pyplot as plt
vector_1 = [3,4]
vector_2 = [1,3]
def get_label(which_vector, vector):
"""Labels a vector with its name and vector head coordinates"""
return f"{which_vector} {vector}"
def plot_line(vector, label):
"""Plots a vector in standard position"""
line = plt.plot([0,vector[0]], [0, vector[1]], label=label)[0]
# Draw as an arrow, as vectors are generally drawn
line.axes.annotate('',
xytext=(0, 0),
xy=(vector[0], vector[1]),
arrowprops=dict(color=line.get_color(), arrowstyle="simple"),
size=10
)
def configure_graph_grid():
"""Set up a square grid axis"""
# Size
grid_min = -6
grid_max = 6
# Make it square and set limits
plt.axis('square')
plt.axis(( grid_min, grid_max, grid_min, grid_max))
# Plot x and y axes
plt.plot([grid_min, grid_max], [0,0], 'g')
plt.plot([0,0],[grid_min, grid_max], 'g')
plt.grid()
# Display the grid and the vectors
figure = plt.figure()
configure_graph_grid()
line_1 = plot_line(vector_1, get_label("Vector 1", vector_1))
line_2 = plot_line(vector_2, get_label("Vector 2", vector_2))
plt.legend()
plt.title('static title')
plt.show()
More discards#
using the return value from the event binding#
cid = fig.canvas.mpl_connect('button_press_event', mouse_event)
more matplotlib show what ya got#
# fig.canvas.flush_events()
# plt.show()