Skip to content

plot

This module contains functions to plot NMR spectra from Bruker data.

bruker1d 

bruker1d(data_paths, labels=None, xlim=None, save=False, filename=None, format=None, frame=False, normalized=False, stacked=False, color=None, return_fig=False)

Plots 1D NMR spectra from Bruker data.

Parameters: data_paths (list): List of paths to the Bruker data directories.

Keyword arguments: labels (list): List of labels for the spectra. xlim (tuple): The limits for the x-axis. save (bool): Whether to save the plot. filename (str): The name of the file to save the plot. format (str): The format to save the file in. frame (bool): Whether to show the frame. normalized (bool): Whether to normalize the spectra. stacked (bool): Whether to stack the spectra. color (str): List of colors for the spectra. return_fig (bool): Whether to return the figure and axis.

Example: bruker1d(['data/1d_data1', 'data/1d_data2'], labels=['Spectrum 1', 'Spectrum 2'], xlim=(0, 100), save=True, filename='1d_spectra', format='png', frame=False, normalized=True, stacked=True, color=['red', 'blue'])

Source code in spinplots/plot.py 
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
def bruker1d(data_paths, labels=None, xlim=None, save=False, filename=None, format=None, frame=False, normalized=False, stacked=False, color=None, return_fig=False):
    """
    Plots 1D NMR spectra from Bruker data.

    Parameters:
        data_paths (list): List of paths to the Bruker data directories.

    Keyword arguments:
        labels (list): List of labels for the spectra.
        xlim (tuple): The limits for the x-axis.
        save (bool): Whether to save the plot.
        filename (str): The name of the file to save the plot.
        format (str): The format to save the file in.
        frame (bool): Whether to show the frame.
        normalized (bool): Whether to normalize the spectra.
        stacked (bool): Whether to stack the spectra.
        color (str): List of colors for the spectra.
        return_fig (bool): Whether to return the figure and axis.

    Example:
        bruker1d(['data/1d_data1', 'data/1d_data2'], labels=['Spectrum 1', 'Spectrum 2'], xlim=(0, 100), save=True, filename='1d_spectra', format='png', frame=False, normalized=True, stacked=True, color=['red', 'blue'])
    """
    fig, ax = plt.subplots()

    nucleus_set = set()

    prev_max = 0
    for i, data_path in enumerate(data_paths):
        dic, data = ng.bruker.read_pdata(data_path)
        udic = ng.bruker.guess_udic(dic, data)

        nuclei = udic[0]['label']

        # Extract the number and nucleus symbol from the label
        number, nucleus = ''.join(filter(str.isdigit, nuclei)), ''.join(filter(str.isalpha, nuclei))

        # Check if the same nucleus is being used
        nucleus_set.add(nucleus)
        if len(nucleus_set) > 1:
            raise ValueError("All the spectra must be of the same nucleus.")

        uc = ng.fileiobase.uc_from_udic(udic, dim=0)
        ppm = uc.ppm_scale()
        ppm_limits = uc.ppm_limits()

        # Normalize the spectrum
        if normalized:
            data = data / np.amax(data)

        # Stack the spectra
        if stacked:
            data += i * 1.1 if normalized else prev_max

        # Plot the spectrum
        if labels and color:
            ax.plot(ppm, data, label=labels[i], color=color[i])
            ax.legend()
        elif labels:
            ax.plot(ppm, data, label=labels[i])
            ax.legend()
        elif color:
            ax.plot(ppm, data, color=color[i])
        else:
            ax.plot(ppm, data)

        prev_max = np.amax(data)

    # Set axis labels with LaTeX formatting and non-italicized letters
    ax.set_xlabel(f'$^{{{number}}}\\mathrm{{{nucleus}}}$ (ppm)', fontsize=13)
    ax.tick_params(axis='x', labelsize=12)

    # Remove frame
    if not frame:
        ax.spines['top'].set_visible(False)
        ax.spines['right'].set_visible(False)
        ax.spines['left'].set_visible(False)
        ax.set_yticklabels([])
        ax.set_yticks([])
    else:
        ax.set_ylabel('Intensity (a.u.)', fontsize=13)
        ax.tick_params(axis='y', labelsize=12)

    # Set axis limits if provided
    if xlim:
        ax.set_xlim(xlim)

    # Show the plot or save it
    if save:
        if filename:
            full_filename = filename + "." + format
        else:
            full_filename = "1d_nmr_spectra." + format
        fig.savefig(full_filename, format=format, dpi=300, bbox_inches='tight', pad_inches=0.1)
    if return_fig:
        return fig, ax
    else:
        plt.show()

bruker1d_grid 

bruker1d_grid(data_paths, labels=None, subplot_dims=(1, 1), xlim=None, save=False, filename=None, format='png', frame=False, normalized=False, color=None, return_fig=False)

Plots 1D NMR spectra from Bruker data in subplots.

Parameters: data_paths (list): List of paths to the Bruker data directories. labels (list): List of labels for the spectra. subplot_dims (tuple): Dimensions of the subplot grid (rows, cols). xlim (list of tuples or tuple): The limits for the x-axis. save (bool): Whether to save the plot. filename (str): The name of the file to save the plot. format (str): The format to save the file in. frame (bool): Whether to show the frame. normalized (bool): Whether to normalize the spectra. color (str): List of colors for the spectra. return_fig (bool): Whether to return the figure and axis.

Example: bruker1d_grid(['data/1d_data1', 'data/1d_data2'], labels=['Spectrum 1', 'Spectrum 2'], subplot_dims=(1, 2), xlim=[(0, 100), (0, 100)], save=True, filename='1d_spectra', format='png', frame=False, normalized=True, color=['red', 'blue'])

Source code in spinplots/plot.py 
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
def bruker1d_grid(data_paths, labels=None, subplot_dims=(1, 1), xlim=None, save=False, filename=None, format='png', frame=False, normalized=False, color=None, return_fig=False):
    """
    Plots 1D NMR spectra from Bruker data in subplots.

    Parameters:
        data_paths (list): List of paths to the Bruker data directories.
        labels (list): List of labels for the spectra.
        subplot_dims (tuple): Dimensions of the subplot grid (rows, cols).
        xlim (list of tuples or tuple): The limits for the x-axis.
        save (bool): Whether to save the plot.
        filename (str): The name of the file to save the plot.
        format (str): The format to save the file in.
        frame (bool): Whether to show the frame.
        normalized (bool): Whether to normalize the spectra.
        color (str): List of colors for the spectra.
        return_fig (bool): Whether to return the figure and axis.

    Example:
        bruker1d_grid(['data/1d_data1', 'data/1d_data2'], labels=['Spectrum 1', 'Spectrum 2'], subplot_dims=(1, 2), xlim=[(0, 100), (0, 100)], save=True, filename='1d_spectra', format='png', frame=False, normalized=True, color=['red', 'blue'])
    """
    rows, cols = subplot_dims
    fig, axes = plt.subplots(rows, cols, figsize=(5 * cols, 4 * rows))
    axes = axes.flatten() if rows * cols > 1 else [axes]

    for i, data_path in enumerate(data_paths):
        if i >= len(axes):
            break
        ax = axes[i]
        dic, data = ng.bruker.read_pdata(data_path)
        udic = ng.bruker.guess_udic(dic, data)

        nuclei = udic[0]['label']
        number, nucleus = ''.join(filter(str.isdigit, nuclei)), ''.join(filter(str.isalpha, nuclei))

        uc = ng.fileiobase.uc_from_udic(udic, dim=0)
        ppm = uc.ppm_scale()

        if normalized:
            data = data / np.amax(data)

        if labels and color:
            ax.plot(ppm, data, label=labels[i], color=color[i])
            ax.legend()
        elif labels:
            ax.plot(ppm, data, label=labels[i])
            ax.legend()
        elif color:
            ax.plot(ppm, data, color=color[i])
        else:
            ax.plot(ppm, data)

        ax.set_xlabel(f'$^{{{number}}}\\mathrm{{{nucleus}}}$ (ppm)', fontsize=13)
        ax.tick_params(axis='x', labelsize=12)

        if not frame:
            ax.spines['top'].set_visible(False)
            ax.spines['right'].set_visible(False)
            ax.spines['left'].set_visible(False)
            ax.set_yticklabels([])
            ax.set_yticks([])
        else:
            ax.set_ylabel('Intensity (a.u.)', fontsize=13)
            ax.tick_params(axis='y', labelsize=12)

        if xlim and isinstance(xlim, tuple):
            ax.set_xlim(xlim)
        elif xlim and isinstance(xlim, list):
            ax.set_xlim(xlim[i])

    plt.tight_layout()

    if save:
        if filename:
            full_filename = filename + "." + format
        else:
            full_filename = "1d_nmr_spectra." + format
        fig.savefig(full_filename, format=format, dpi=300, bbox_inches='tight', pad_inches=0.1)
    elif return_fig:
        return fig, axes
    else:
        plt.show()

bruker2d 

bruker2d(data_path, contour_start, contour_num, contour_factor, cmap=None, xlim=None, ylim=None, save=False, filename=None, format=None, diag=None, homo=False, return_fig=False)

Plots a 2D NMR spectrum from Bruker data.

Parameters: data_path (str): Path to the Bruker data directory. contour_start (float): The starting value for the contour lines. contour_num (int): The number of contour lines. contour_factor (float): The factor by which the contour levels increase.

Keyword arguments: cmap (str): The colormap to use for the contour lines. xlim (tuple): The limits for the x-axis. ylim (tuple): The limits for the y-axis. save (bool): Whether to save the plot. filename (str): The name of the file to save the plot. format (str): The format to save the file in. diag (float or None): Slope of the diagonal line/None. homo (bool): True if doing homonuclear experiment. return_fig (bool): Whether to return the figure and axis.

Example: bruker2d('data/2d_data', 0.1, 10, 1.2, cmap='viridis', xlim=(0, 100), ylim=(0, 100), save=True, filename='2d_spectrum', format='png', diag=True)

Source code in spinplots/plot.py 
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
def bruker2d(data_path, contour_start, contour_num, contour_factor, cmap=None, xlim=None, ylim=None, save=False, filename=None, format=None, diag=None, homo=False, return_fig=False):
    """
    Plots a 2D NMR spectrum from Bruker data.

    Parameters:
        data_path (str): Path to the Bruker data directory.
        contour_start (float): The starting value for the contour lines.
        contour_num (int): The number of contour lines.
        contour_factor (float): The factor by which the contour levels increase.

    Keyword arguments:
        cmap (str): The colormap to use for the contour lines.
        xlim (tuple): The limits for the x-axis.
        ylim (tuple): The limits for the y-axis.
        save (bool): Whether to save the plot.
        filename (str): The name of the file to save the plot.
        format (str): The format to save the file in.
        diag (float or None): Slope of the diagonal line/None.
        homo (bool): True if doing homonuclear experiment.
        return_fig (bool): Whether to return the figure and axis.

    Example:
        bruker2d('data/2d_data', 0.1, 10, 1.2, cmap='viridis', xlim=(0, 100), ylim=(0, 100), save=True, filename='2d_spectrum', format='png', diag=True)
    """
    dic, data = ng.bruker.read_pdata(data_path)
    udic = ng.bruker.guess_udic(dic, data)

    # Check if homo is set to True
    if homo:
        nuclei_x = udic[1]['label']
        nuclei_y = udic[1]['label']
    else: 
        nuclei_x = udic[1]['label']
        nuclei_y = udic[0]['label']

    # Extract the number and nucleus symbol from the label
    number_x, nucleus_x = ''.join(filter(str.isdigit, nuclei_x)), ''.join(filter(str.isalpha, nuclei_x))
    number_y, nucleus_y = ''.join(filter(str.isdigit, nuclei_y)), ''.join(filter(str.isalpha, nuclei_y))

    uc_x = ng.fileiobase.uc_from_udic(udic, dim=1)
    ppm_x = uc_x.ppm_scale()
    ppm_x_limits = uc_x.ppm_limits()
    proj_x = np.amax(data, axis=0)

    uc_y = ng.fileiobase.uc_from_udic(udic, dim=0)
    ppm_y = uc_y.ppm_scale()
    ppm_y_limits = uc_y.ppm_limits()
    proj_y = np.amax(data, axis=1)

    # Create figure and axis
    ax = plt.figure(constrained_layout=False).subplot_mosaic(
    """
    .a
    bA
    """,
    gridspec_kw={"height_ratios": [0.9, 6.0], "width_ratios": [0.8, 6.0], 'wspace': 0.03, 'hspace': 0.04},
    )   

    # Contour levels
    contour_levels = contour_start * contour_factor ** np.arange(contour_num)

    # Plot contour lines with the provided colormap if cmap is provided
    if cmap is not None:
        contour_plot = ax['A'].contour(data, contour_levels, extent=(ppm_x_limits[0], ppm_x_limits[1], ppm_y_limits[0], ppm_y_limits[1]), cmap=cmap, linewidths=0.5)
        darkest_color = contour_plot.collections[0].get_edgecolor()[0]  # Get the color of the first contour line
    else:
        darkest_color = 'black'
        contour_plot = ax['A'].contour(data, contour_levels, extent=(ppm_x_limits[0], ppm_x_limits[1], ppm_y_limits[0], ppm_y_limits[1]), colors = 'black', linewidths=0.5)

    # Plot projections with the extracted color
    ax['a'].plot(ppm_x, proj_x, linewidth=0.8, color=darkest_color)
    ax['a'].axis(False)
    ax['b'].plot(-proj_y, ppm_y, linewidth=0.8, color=darkest_color)
    ax['b'].axis(False)

    # Set axis labels with LaTeX formatting and non-italicized letters and position
    ax['A'].set_xlabel(f'$^{{{number_x}}}\\mathrm{{{nucleus_x}}}$ (ppm)', fontsize=13)
    ax['A'].set_ylabel(f'$^{{{number_y}}}\\mathrm{{{nucleus_y}}}$ (ppm)', fontsize=13)
    ax['A'].yaxis.set_label_position('right')
    ax['A'].yaxis.tick_right()
    ax['A'].tick_params(axis='x', labelsize=12)
    ax['A'].tick_params(axis='y', labelsize=12)

    # Plot diagonal line if diag is provided
    if diag is not None:
        x_diag = np.linspace(ppm_x_limits[0], ppm_x_limits[1], 100)
        y_diag = diag * x_diag
        ax['A'].plot(x_diag, y_diag, linestyle='--', color='gray')

    # Set axis limits if provided
    if xlim:
        ax['A'].set_xlim(xlim)
        ax['a'].set_xlim(xlim)
    if ylim:
        ax['A'].set_ylim(ylim)
        ax['b'].set_ylim(ylim)


    # Show the plot or save it
    if save:
        if filename:
            full_filename = filename + "." + format
        else:
            full_filename = "2d_nmr_spectrum." + format
        plt.savefig(full_filename, format=format, dpi=300, bbox_inches='tight', pad_inches=0.1)

    if return_fig:
        return ax
    else:
        plt.show()