Completedly overhauled fig_auditory_pathway.
Created new in-figure linked SVGs with clearer naming scheme. Removed outdated image components.
This commit is contained in:
j-hartling
@@ -8,9 +8,12 @@ plt.rcParams['svg.fonttype'] = 'path'
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plt.rc('text.latex', preamble=r'\usepackage{amsmath}')
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# Settings:
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fs_chart = 70
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fs_lines = 75
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fs_legend = 40
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titles_fontsize = 50
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titles_figsize = (4, 2.5)
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elements_fontsize = 80
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elements_figsize = (3, 3)
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lines_fontsize = 80
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lines_figsize = (3, 3)
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show_figs = True
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grid_props = dict(left=0, right=1, top=1, bottom=0)
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@@ -35,51 +38,56 @@ text_props = {
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# Targets:
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texts = {
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# CIRCUIT ELEMENTS:
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# 'filt': (r'$x_{\text{filt}}$', fs_chart, (3, 3)),
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# 'env': (r'$x_{\text{env}}$', fs_chart, (3, 3)),
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# 'db': (r'$x_{\text{dB}}$', fs_chart, (3, 3)),
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# 'envdb': (r'$\begin{array}{c}x_{\text{env}}\\x_{\text{dB}}\end{array}$', fs_chart, (3, 3)),
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# 'adapt': (r'$x_{\text{adapt}}$', fs_chart, (3, 3)),
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# 'conv1': (r'$c_1$', fs_chart, (3, 3)),
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# 'conv2': (r'$c_2$', fs_chart, (3, 3)),
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# 'conv3': (r'$c_3$', fs_chart, (3, 3)),
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# 'bi1': (r'$b_{1, \Theta}$', fs_chart, (3, 3)),
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# 'bi2': (r'$b_{2, \Theta}$', fs_chart, (3, 3)),
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# 'bi3': (r'$b_{3, \Theta}$', fs_chart, (3, 3)),
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# 'feat1': (r'$f_{1, \Theta}$', fs_chart, (3, 3)),
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# 'feat2': (r'$f_{2, \Theta}$', fs_chart, (3, 3)),
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# 'feat3': (r'$f_{3, \Theta}$', fs_chart, (3, 3)),
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# 'out': (r'$\hat{y}$', fs_chart, (3, 3)),
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# # TITLES (NEURONAL CIRCUIT):
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# 'neuron_titles_tympanum': ('Tympanal\nMembrane', titles_fontsize, titles_figsize),
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# 'neuron_titles_receptors': ('Receptor\nNeurons', titles_fontsize, titles_figsize),
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# 'neuron_titles_interneurons': ('Local\nInterneurons', titles_fontsize, titles_figsize),
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# 'neuron_titles_ascending': ('Ascending\nNeurons', titles_fontsize, titles_figsize),
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# 'neuron_titles_brain': ('Central\nBrain', titles_fontsize, titles_figsize),
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# CIRCUIT OPERATIONS:
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'rectlp': (r'$\lvert\cdot\lvert,h_{\text{LP}}$', fs_lines, (3, 3)),
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'log': (r'$\text{log}$', fs_lines, (3, 3)),
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'rectlplog': (r'$\begin{array}{c}||,h_{\text{LP}}\\\text{log}\end{array}$', fs_lines, (3, 3)),
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'k1': (r'$k_1$', fs_lines, (3, 3)),
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'k2': (r'$k_2$', fs_lines, (3, 3)),
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'k3': (r'$k_3$', fs_lines, (3, 3)),
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'bp': (r'$h_{\text{BP}}$', fs_lines, (3, 3)),
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'lp': (r'$h_{\text{LP}}$', fs_lines, (3, 3)),
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'hp': (r'$h_{\text{HP}}$', fs_lines, (3, 3)),
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'theta1': (r'$\Theta_1$', fs_lines, (3, 3)),
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'theta2': (r'$\Theta_2$', fs_lines, (3, 3)),
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'theta3': (r'$\Theta_3$', fs_lines, (3, 3)),
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'w1': (r'$\omega_1$', fs_lines, (3, 3)),
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'w2': (r'$\omega_2$', fs_lines, (3, 3)),
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'w3': (r'$\omega_3$', fs_lines, (3, 3)),
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'thresh1': (r'$H(c_1-\Theta_1)$', fs_lines, (3, 3)),
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'thresh2': (r'$H(c_2-\Theta_2)$', fs_lines, (3, 3)),
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'thresh3': (r'$H(c_3-\Theta_3)$', fs_lines, (3, 3)),
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# # TITLES (MODEL CIRCUIT):
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# 'model_titles_bandpass': ('Bandpass\nFiltering', titles_fontsize, titles_figsize),
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# 'model_titles_envelope': ('Envelope\nExtraction', titles_fontsize, titles_figsize),
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# 'model_titles_logarithm': ('Logarithmic\nCompression', titles_fontsize, titles_figsize),
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# 'model_titles_adaptation': ('Intensity\nAdaptation', titles_fontsize, titles_figsize),
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# 'model_titles_convolution': ('Convolutional\nFiltering', titles_fontsize, titles_figsize),
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# 'model_titles_nonlinear': ('Threshold\nNonlinearity', titles_fontsize, titles_figsize),
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# 'model_titles_integration': ('Temporal\nAveraging', titles_fontsize, titles_figsize),
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# 'model_titles_readout': ('Weighted\nReadout', titles_fontsize, titles_figsize),
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# LEGEND ELEMENTS:
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# 'tympanum': ('Tympanal\nMembrane', fs_legend, (3, 3)),
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# 'receptors': ('Receptor\nNeuron\nPopulation', fs_legend, (3, 3)),
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# 'interneurons': ('Local\nInterneuron\nPopulation', fs_legend, (3, 3)),
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# 'ascending': ('Individual\nAscending\nNeurons', fs_legend, (3, 3)),
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# 'threshold': ('Threshold\nNonlinearity', fs_legend, (3, 3)),
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# 'integration': ('Temporal\nAveraging', fs_legend, (3, 3)),
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# 'brain': ('Weighting\nand\nReadout', fs_legend, (3, 3)),
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# # ELEMENT LABELS (MODEL CIRCUIT):
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# 'model_elements_filt': (r'$x_{\text{filt}}$', elements_fontsize, elements_figsize),
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# 'model_elements_env': (r'$x_{\text{env}}$', elements_fontsize, elements_figsize),
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# 'model_elements_log': (r'$x_{\text{dB}}$', elements_fontsize, elements_figsize),
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# 'model_elements_adapt': (r'$x_{\text{adapt}}$', elements_fontsize, elements_figsize),
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# 'model_elements_c1': (r'$c_1$', elements_fontsize, elements_figsize),
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# 'model_elements_c2': (r'$c_2$', elements_fontsize, elements_figsize),
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# 'model_elements_c3': (r'$c_3$', elements_fontsize, elements_figsize),
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# 'model_elements_b1': (r'$b_1$', elements_fontsize, elements_figsize),
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# 'model_elements_b2': (r'$b_2$', elements_fontsize, elements_figsize),
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# 'model_elements_b3': (r'$b_3$', elements_fontsize, elements_figsize),
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# 'model_elements_f1': (r'$f_1$', elements_fontsize, elements_figsize),
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# 'model_elements_f2': (r'$f_2$', elements_fontsize, elements_figsize),
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# 'model_elements_f3': (r'$f_3$', elements_fontsize, elements_figsize),
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# 'model_elements_out': (r'$\hat{y}$', elements_fontsize, elements_figsize),
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# LINE LABELS (MODEL CIRCUIT):
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'model_lines_env': (r'$\lvert\cdot\lvert,h_{\text{LP}}$', lines_fontsize, lines_figsize),
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'model_lines_log': (r'$\text{log}$', lines_fontsize, lines_figsize),
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'model_lines_hp': (r'$h_{\text{HP}}$', lines_fontsize, lines_figsize),
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'model_lines_k1': (r'$k_1$', lines_fontsize, lines_figsize),
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'model_lines_k2': (r'$k_2$', lines_fontsize, lines_figsize),
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'model_lines_k3': (r'$k_3$', lines_fontsize, lines_figsize),
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'model_lines_t1': (r'$\Theta_1$', lines_fontsize, lines_figsize),
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'model_lines_t2': (r'$\Theta_2$', lines_fontsize, lines_figsize),
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'model_lines_t3': (r'$\Theta_3$', lines_fontsize, lines_figsize),
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# 'model_lines_H1': (r'$H(c_1-\Theta_1)$', lines_fontsize, lines_figsize),
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# 'model_lines_H2': (r'$H(c_2-\Theta_2)$', lines_fontsize, lines_figsize),
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# 'model_lines_H3': (r'$H(c_3-\Theta_3)$', lines_fontsize, lines_figsize),
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'model_lines_lp': (r'$h_{\text{LP}}$', lines_fontsize, lines_figsize),
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'model_lines_w1': (r'$\omega_1$', lines_fontsize, lines_figsize),
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'model_lines_w2': (r'$\omega_2$', lines_fontsize, lines_figsize),
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'model_lines_w3': (r'$\omega_3$', lines_fontsize, lines_figsize),
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}
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# Save each target string:
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@@ -93,135 +101,3 @@ for name, (text, fs, size) in texts.items():
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if show_figs:
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plt.show()
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plt.close(fig)
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# def text_box(ax, text, xy, width, height, transform=None,
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# ha='center', va='center', **kwargs):
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# """ Maximizes fontsize of text to fit into a given bounding box.
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# Calculated fontsize depends on the aspect ratio of the bounding box,
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# the aspect ratio and alignment of the text, and the resolution and size
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# of the underlying figure. Text is not updated when resizing the figure.
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# Parameters
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# ----------
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# ax : matplotlib axes object
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# Target subplot to annotate the text.
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# text : str
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# Text to fit into the specified bounding box under the given alignments.
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# xy : tuple of floats or ints (2,)
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# Text position in the coordinate system specified by transform.
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# width : float or int
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# Rectangle width in the coordinate system specified by transform.
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# height : float or int
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# Rectangle height in the coordinate system specified by transform.
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# transform : matplotlib transform, optional
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# Underlying coordinate system of the bounding box. Determines the
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# interpretation of xy, width, and height. Falls back to data coordinates
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# if unspecified. The default is None.
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# ha : str, optional
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# Horizontal alignment of bounding box and text relative to the given xy.
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# The default is 'center'.
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# va : str, optional
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# Vertical alignment of bounding box and text relative to the given xy.
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# The default is 'center'.
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# **kwargs : dict, optional
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# Additional keyword arguments passed to ax.annotate() for specifying
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# different font properties of the returned text object.
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# Returns
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# -------
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# t : matplotlib text object
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# Annotated text object with adjusted fontsize to fit the bounding box.
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# """
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# # Input interpretation:
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# if transform is None:
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# transform = ax.transData
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# fig = ax.get_figure()
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# x, y = xy
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# # Alignment-specific anchor points:
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# x_align1, x_align2 = {
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# 'center': (x - width / 2, x + width / 2),
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# 'left': (x, x + width),
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# 'right': (x - width, x),
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# }[ha]
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# y_align1, y_align2 = {
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# 'center': (y - height / 2, y + height / 2),
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# 'bottom': (y, y + height),
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# 'top': (y - height, y),
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# }[va]
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# # Anchor points in pixel:
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# left_corner = transform.transform((x_align1, y_align1))
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# right_corner = transform.transform((x_align2, y_align2))
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# # Bounding rectangle size in pixel:
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# pixel_width = right_corner[0] - left_corner[0]
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# pixel_height = right_corner[1] - left_corner[1]
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# # Adjust fontsize to box height (inch):
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# dpi = fig.dpi
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# rect_height = pixel_height / dpi
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# fs_initial = rect_height * 72
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# # Plot first draft of the text:
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# t = ax.annotate(text, xy, ha=ha, va=va, xycoords=transform, **kwargs)
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# t.set_fontsize(fs_initial)
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# # Adjust fontsize to box width (inch):
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# bbox = t.get_window_extent(fig.canvas.get_renderer())
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# fs_adjusted = fs_initial * pixel_width / bbox.width
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# t.set_fontsize(fs_adjusted)
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# return t
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# def text_graph(text, save_str=None, size=None, ax=None, show=False,
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# close=False, **kwargs):
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# """ Turns entire subplot into a text box that displays the given text.
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# Fontsize is maximized to fit the available bounding box. Text is always
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# centered in the subplot. Meant for creating scalable text elements that
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# comply with the style of other plot elements, especially for posters.
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# Parameters
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# ----------
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# text : str
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# Text to be displayed. Can be multiline. Text fontsize is maximized by
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# text_box() to fit a bounding box that covers the entire axes area.
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# save_str : str, optional
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# If specified, saves the underlying figure under the given path. For
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# best results, use a vector format such as .svg). The default is None.
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# size : tuple of floats or ints (2,), optional
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# If specified, creates a new figure with given size in inches and a
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# single subplot. Indirectly controls the aspect ratio of the text box.
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# Must be specified if ax is None. The default is None.
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# ax : matplotlib axes object, optional
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# If specified, the target subplot to turn into a text box. Can be used
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# to set more properties such as the background color of the text box.
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# Must be specified if size is None. The default is None.
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# show : bool, optional
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# If True, displays the figure before returning. Else, returns without
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# showing the figure. The default is False.
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# **kwargs : dict, optional
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# Keyword arguments passed to text_box() and further to ax.annotate() for
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# specifying additional font properties of the displayed text.
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# Raises
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# ------
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# ValueError
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# Breaks if neither size nor ax is specified to define a target subplot.
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# """
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# # Input interpretation:
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# if size is not None:
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# fig, ax = plt.subplots(figsize=size)
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# elif ax is not None:
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# fig = ax.get_figure()
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# else:
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# raise ValueError('Either size or ax must be specified.')
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# # Turn drawable area of axes into a single text box:
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# text_box(ax, text, (0.5, 0.5), 1, 1, ax.transAxes, **kwargs)
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# # Hide other axes elements:
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# ax.xaxis.set_visible(False)
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# ax.yaxis.set_visible(False)
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# ax.spines[:].set_visible(False)
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# # Return options:
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# if save_str is not None:
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# fig.savefig(save_str, bbox_inches='tight')
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# if show:
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# plt.show()
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# return None
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24
python/solve_post_problem.py
Normal file
24
python/solve_post_problem.py
Normal file
@@ -0,0 +1,24 @@
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import numpy as np
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# NEURON CIRCUIT (WIDTH):
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# total_space = 32
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# side_space = 0.5
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# n_posts = 5
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# MODEL CIRCUIT (WIDTH):
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total_space = 48
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side_space = 0.5
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n_posts = 8
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# RUN SOLVER:
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post_space = np.arange(1, (total_space - 2 * side_space) // n_posts + 1)
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available_space = total_space - 2 * side_space - n_posts * post_space
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inter_space = available_space / (n_posts - 1)
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remaining_space = available_space % (n_posts - 1)
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# REPORT OUTCOMES:
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print('\nPost Width - Inter-Post - Remainder:')
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for input, output, remainder in zip(post_space, inter_space, remaining_space):
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print(input, output, remainder)
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print()
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