Skip to content

Visualization

Plotting functions for displacement fields, InSAR products, and time series analysis. Requires matplotlib (pip install eq-insar[viz]).

Displacement

plot_displacement_components 

plot_displacement_components(result: Dict, title_prefix: str = '', figsize: Tuple[float, float] = (14, 12), cmap: str = 'RdBu_r', show_epicenter: bool = True) -> plt.Figure

Plot all three displacement components and LOS.

Creates a 2x2 grid showing East, North, Up, and LOS displacement.

PARAMETER DESCRIPTION
result

Output from generate_synthetic_insar or generate_timeseries

TYPE: dict

title_prefix

Prefix for the plot title

TYPE: str DEFAULT: ''

figsize

Figure size in inches

TYPE: tuple DEFAULT: (14, 12)

cmap

Colormap for displacement

TYPE: str DEFAULT: 'RdBu_r'

show_epicenter

Show epicenter location as star

TYPE: bool DEFAULT: True

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/displacement.py 
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
def plot_displacement_components(
    result: Dict,
    title_prefix: str = "",
    figsize: Tuple[float, float] = (14, 12),
    cmap: str = "RdBu_r",
    show_epicenter: bool = True,
) -> plt.Figure:
    """
    Plot all three displacement components and LOS.

    Creates a 2x2 grid showing East, North, Up, and LOS displacement.

    Parameters
    ----------
    result : dict
        Output from generate_synthetic_insar or generate_timeseries
    title_prefix : str
        Prefix for the plot title
    figsize : tuple
        Figure size in inches
    cmap : str
        Colormap for displacement
    show_epicenter : bool
        Show epicenter location as star

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    fig, axes = plt.subplots(2, 2, figsize=figsize)

    X = result["X_km"]
    Y = result["Y_km"]
    meta = result["metadata"]

    components = [
        ("Ue", "East displacement (cm)"),
        ("Un", "North displacement (cm)"),
        ("Uz", "Vertical displacement (cm)"),
        ("los_displacement", "LOS displacement (cm)"),
    ]

    for ax, (key, label) in zip(axes.flat, components):
        data = result[key] * 100  # Convert to cm
        vmax = np.percentile(np.abs(data), 99)
        vmax = max(vmax, 0.1)  # Minimum scale

        im = ax.contourf(
            X, Y, data, levels=50, cmap=cmap, vmin=-vmax, vmax=vmax
        )
        plt.colorbar(im, ax=ax, label=label)
        ax.set_xlabel("X (km)")
        ax.set_ylabel("Y (km)")
        ax.set_aspect("equal")

        if show_epicenter:
            ax.plot(meta["xcen_km"], meta["ycen_km"], "k*", markersize=12)

    # Build title
    title_parts = [title_prefix] if title_prefix else []
    title_parts.append(f"Mw={meta['Mw']:.1f}")
    title_parts.append(f"Strike={meta['strike_deg']:.0f}°")
    title_parts.append(f"Dip={meta['dip_deg']:.0f}°")
    title_parts.append(f"Rake={meta['rake_deg']:.0f}°")
    title_parts.append(f"Depth={meta['depth_km']:.1f}km")
    title_parts.append(f"({meta.get('source_type', 'unknown').upper()})")

    fig.suptitle(" | ".join(title_parts), fontsize=12)
    plt.tight_layout()
    return fig

plot_insar_products 

plot_insar_products(result: Dict, title_prefix: str = '', figsize: Tuple[float, float] = (14, 4), phase_cmap: str = 'hsv') -> plt.Figure

Plot InSAR-specific products: phase and LOS displacement.

PARAMETER DESCRIPTION
result

Output from generate_synthetic_insar

TYPE: dict

title_prefix

Prefix for the plot title

TYPE: str DEFAULT: ''

figsize

Figure size in inches

TYPE: tuple DEFAULT: (14, 4)

phase_cmap

Colormap for wrapped phase (default: hsv for cyclic)

TYPE: str DEFAULT: 'hsv'

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/displacement.py 
 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
118
119
120
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
def plot_insar_products(
    result: Dict,
    title_prefix: str = "",
    figsize: Tuple[float, float] = (14, 4),
    phase_cmap: str = "hsv",
) -> plt.Figure:
    """
    Plot InSAR-specific products: phase and LOS displacement.

    Parameters
    ----------
    result : dict
        Output from generate_synthetic_insar
    title_prefix : str
        Prefix for the plot title
    figsize : tuple
        Figure size in inches
    phase_cmap : str
        Colormap for wrapped phase (default: hsv for cyclic)

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    fig, axes = plt.subplots(1, 3, figsize=figsize)

    X = result["X_km"]
    Y = result["Y_km"]
    meta = result["metadata"]
    extent = [X.min(), X.max(), Y.min(), Y.max()]

    # Wrapped phase (clean)
    im0 = axes[0].imshow(
        result["phase_wrapped"],
        extent=extent,
        origin="lower",
        cmap=phase_cmap,
        vmin=-np.pi,
        vmax=np.pi,
    )
    plt.colorbar(im0, ax=axes[0], label="Phase (rad)")
    axes[0].set_title("Wrapped Phase (Clean)")
    axes[0].set_xlabel("X (km)")
    axes[0].set_ylabel("Y (km)")

    # Wrapped phase (noisy)
    im1 = axes[1].imshow(
        result["phase_noisy"],
        extent=extent,
        origin="lower",
        cmap=phase_cmap,
        vmin=-np.pi,
        vmax=np.pi,
    )
    plt.colorbar(im1, ax=axes[1], label="Phase (rad)")
    axes[1].set_title("Wrapped Phase (With Noise)")
    axes[1].set_xlabel("X (km)")

    # LOS displacement
    los_cm = result["los_displacement"] * 100
    vmax_los = np.percentile(np.abs(los_cm), 99)
    im2 = axes[2].imshow(
        los_cm,
        extent=extent,
        origin="lower",
        cmap="RdBu_r",
        vmin=-vmax_los,
        vmax=vmax_los,
    )
    plt.colorbar(im2, ax=axes[2], label="LOS (cm)")
    axes[2].set_title("LOS Displacement")
    axes[2].set_xlabel("X (km)")

    # Title with satellite info
    sat_info = f"{meta.get('satellite', 'N/A')} {meta.get('orbit', '')}"
    fig.suptitle(
        f"{title_prefix}Mw={meta['Mw']:.1f} | {sat_info} | "
        f"λ={meta['wavelength_m']*100:.1f}cm",
        fontsize=12,
    )
    plt.tight_layout()
    return fig

Time Series

plot_timeseries_frames 

plot_timeseries_frames(result: Dict, n_cols: int = 4, figsize_per_frame: float = 3.0, phase_cmap: str = 'hsv') -> plt.Figure

Plot time series frames as spatial maps.

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

n_cols

Number of columns in the grid

TYPE: int DEFAULT: 4

figsize_per_frame

Figure size per frame in inches

TYPE: float DEFAULT: 3.0

phase_cmap

Colormap for phase data

TYPE: str DEFAULT: 'hsv'

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
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
def plot_timeseries_frames(
    result: Dict,
    n_cols: int = 4,
    figsize_per_frame: float = 3.0,
    phase_cmap: str = "hsv",
) -> plt.Figure:
    """
    Plot time series frames as spatial maps.

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    n_cols : int
        Number of columns in the grid
    figsize_per_frame : float
        Figure size per frame in inches
    phase_cmap : str
        Colormap for phase data

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    ts = result["timeseries"]
    n_frames = ts.shape[0]
    n_rows = int(np.ceil(n_frames / n_cols))

    figsize = (figsize_per_frame * n_cols, figsize_per_frame * n_rows)
    fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)
    axes = axes.flatten()

    meta = result["metadata"]
    n_pre = meta["n_pre"]
    n_event = meta["n_event"]

    for i in range(n_frames):
        if i < n_pre:
            title = f"Pre-event {i+1}"
            color = "blue"
        elif i < n_pre + n_event:
            title = f"Event {i - n_pre + 1}"
            color = "red"
        else:
            title = f"Post-event {i - n_pre - n_event + 1}"
            color = "green"

        im = axes[i].imshow(ts[i], cmap=phase_cmap, vmin=-np.pi, vmax=np.pi)
        axes[i].set_title(title, fontsize=10, color=color, fontweight="bold")
        axes[i].axis("off")

    for i in range(n_frames, len(axes)):
        axes[i].axis("off")

    plt.suptitle(
        f"Time Series Frames | Mw={meta['Mw']:.1f} | {meta['source_type'].upper()}",
        fontsize=12,
    )
    plt.tight_layout()
    return fig

plot_timeseries_statistics 

plot_timeseries_statistics(result: Dict, figsize: Tuple[float, float] = (14, 10)) -> plt.Figure

Plot time series statistics over time.

Shows max, mean, std, and circular variance evolution through pre-event, event, and post-event phases.

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

figsize

Figure size

TYPE: tuple DEFAULT: (14, 10)

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
 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
118
119
120
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
def plot_timeseries_statistics(
    result: Dict,
    figsize: Tuple[float, float] = (14, 10),
) -> plt.Figure:
    """
    Plot time series statistics over time.

    Shows max, mean, std, and circular variance evolution through
    pre-event, event, and post-event phases.

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    figsize : tuple
        Figure size

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    ts = result["timeseries"]
    meta = result["metadata"]
    n_frames = ts.shape[0]
    n_pre = meta["n_pre"]
    n_event = meta["n_event"]
    output_type = meta.get("output_type", "phase")

    # Compute statistics
    max_vals = np.array([np.max(np.abs(ts[t])) for t in range(n_frames)])
    mean_vals = np.array([np.mean(ts[t]) for t in range(n_frames)])
    std_vals = np.array([np.std(ts[t]) for t in range(n_frames)])

    # Circular variance for phase
    if output_type == "phase":
        signal_power = np.array(
            [1 - np.abs(np.mean(np.exp(1j * ts[t]))) for t in range(n_frames)]
        )
    else:
        signal_power = std_vals**2

    time_indices = np.arange(n_frames)
    fig, axes = plt.subplots(2, 2, figsize=figsize)

    # Phase regions
    for ax in axes.flat:
        ax.axvspan(-0.5, n_pre - 0.5, alpha=0.2, color="blue", label="Pre-event")
        ax.axvspan(n_pre - 0.5, n_pre + n_event - 0.5, alpha=0.3, color="red", label="Event")
        ax.axvspan(n_pre + n_event - 0.5, n_frames - 0.5, alpha=0.2, color="green", label="Post-event")

    unit = " (rad)" if output_type == "phase" else " (m)"

    axes[0, 0].plot(time_indices, max_vals, "ko-", linewidth=2, markersize=8)
    axes[0, 0].set_ylabel(f"Max |value|{unit}")
    axes[0, 0].set_title("Maximum Absolute Value")
    axes[0, 0].grid(True, alpha=0.3)

    axes[0, 1].plot(time_indices, mean_vals, "ko-", linewidth=2, markersize=8)
    axes[0, 1].set_ylabel(f"Mean value{unit}")
    axes[0, 1].set_title("Mean Value")
    axes[0, 1].grid(True, alpha=0.3)

    axes[1, 0].plot(time_indices, std_vals, "ko-", linewidth=2, markersize=8)
    axes[1, 0].set_ylabel(f"Std dev{unit}")
    axes[1, 0].set_title("Standard Deviation")
    axes[1, 0].grid(True, alpha=0.3)

    axes[1, 1].plot(time_indices, signal_power, "ko-", linewidth=2, markersize=8)
    if output_type == "phase":
        axes[1, 1].set_ylabel("Circular variance")
        axes[1, 1].set_title("Phase Dispersion")
    else:
        axes[1, 1].set_ylabel("Variance (m²)")
        axes[1, 1].set_title("Signal Variance")
    axes[1, 1].grid(True, alpha=0.3)

    for ax in axes.flat:
        ax.set_xlabel("Frame")
        ax.set_xticks(time_indices)

    axes[0, 0].legend(loc="upper right", fontsize=9)
    fig.suptitle(f"Time Series Statistics | Mw={meta['Mw']:.1f}", fontsize=12)
    plt.tight_layout()
    return fig

plot_timeseries_at_points 

plot_timeseries_at_points(result: Dict, points_km: Optional[List[Tuple[float, float]]] = None, figsize: Tuple[float, float] = (14, 10)) -> plt.Figure

Plot time series signal at specific pixel locations.

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

points_km

List of (x_km, y_km) locations. If None, uses epicenter and nearby.

TYPE: list of tuples DEFAULT: None

figsize

Figure size

TYPE: tuple DEFAULT: (14, 10)

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
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
218
219
220
221
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
def plot_timeseries_at_points(
    result: Dict,
    points_km: Optional[List[Tuple[float, float]]] = None,
    figsize: Tuple[float, float] = (14, 10),
) -> plt.Figure:
    """
    Plot time series signal at specific pixel locations.

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    points_km : list of tuples, optional
        List of (x_km, y_km) locations. If None, uses epicenter and nearby.
    figsize : tuple
        Figure size

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    ts = result["timeseries"]
    X_km = result["X_km"]
    Y_km = result["Y_km"]
    meta = result["metadata"]
    output_type = meta.get("output_type", "phase")

    n_frames = ts.shape[0]
    n_pre = meta["n_pre"]
    n_event = meta["n_event"]

    # Default points
    if points_km is None:
        xcen = meta["xcen_km"]
        ycen = meta["ycen_km"]
        offset = 10.0
        points_km = [
            (xcen, ycen),
            (xcen + offset, ycen),
            (xcen - offset, ycen),
            (xcen, ycen + offset),
            (xcen, ycen - offset),
        ]
        point_labels = ["Epicenter", "East +10km", "West -10km", "North +10km", "South -10km"]
    else:
        point_labels = [f"({x:.1f}, {y:.1f})" for x, y in points_km]

    def km_to_pixel(x_km, y_km):
        dx = np.abs(X_km[0, :] - x_km)
        dy = np.abs(Y_km[:, 0] - y_km)
        return np.argmin(dy), np.argmin(dx)

    time_indices = np.arange(n_frames)
    fig, axes = plt.subplots(2, 1, figsize=figsize)
    colors = plt.cm.tab10(np.linspace(0, 1, len(points_km)))

    # Time series plot
    ax1 = axes[0]
    for i, ((x_km, y_km), label) in enumerate(zip(points_km, point_labels)):
        iy, ix = km_to_pixel(x_km, y_km)
        signal = ts[:, iy, ix]
        ax1.plot(time_indices, signal, "o-", color=colors[i], linewidth=2,
                 markersize=6, label=label)

    ax1.axvspan(-0.5, n_pre - 0.5, alpha=0.15, color="blue")
    ax1.axvspan(n_pre - 0.5, n_pre + n_event - 0.5, alpha=0.2, color="red")
    ax1.axvspan(n_pre + n_event - 0.5, n_frames - 0.5, alpha=0.15, color="green")

    ax1.set_xlabel("Frame")
    ax1.set_ylabel("Signal" + (" (rad)" if output_type == "phase" else " (m)"))
    ax1.set_title("Signal at Specific Locations")
    ax1.legend(loc="upper right")
    ax1.grid(True, alpha=0.3)
    ax1.set_xticks(time_indices)

    # Map with points
    ax2 = axes[1]
    event_frame = ts[n_pre]
    extent = [X_km.min(), X_km.max(), Y_km.min(), Y_km.max()]

    if output_type == "phase":
        im = ax2.imshow(event_frame, extent=extent, origin="lower",
                        cmap="hsv", vmin=-np.pi, vmax=np.pi)
        plt.colorbar(im, ax=ax2, label="Phase (rad)")
    else:
        vmax = np.percentile(np.abs(event_frame), 99)
        im = ax2.imshow(event_frame, extent=extent, origin="lower",
                        cmap="RdBu_r", vmin=-vmax, vmax=vmax)
        plt.colorbar(im, ax=ax2, label="Displacement (m)")

    for i, ((x_km, y_km), label) in enumerate(zip(points_km, point_labels)):
        ax2.plot(x_km, y_km, "o", color=colors[i], markersize=12,
                 markeredgecolor="white", markeredgewidth=2)
        ax2.annotate(f"{i+1}", (x_km, y_km), color="white", fontsize=8,
                     ha="center", va="center", fontweight="bold")

    ax2.plot(meta["xcen_km"], meta["ycen_km"], "k*", markersize=15)
    ax2.set_xlabel("X (km)")
    ax2.set_ylabel("Y (km)")
    ax2.set_title(f"Event Frame (t={n_pre})")
    ax2.set_aspect("equal")

    fig.suptitle(f"Time Series at Points | Mw={meta['Mw']:.1f}", fontsize=12)
    plt.tight_layout()
    return fig

plot_timeseries_displacement_components 

plot_timeseries_displacement_components(result: Dict, point_km: Optional[Tuple[float, float]] = None, figsize: Tuple[float, float] = (16, 12)) -> plt.Figure

Plot displacement components (LOS, Uz, Ue, Un) over time at a point.

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

point_km

Location. If None, uses max |LOS| location.

TYPE: tuple(x_km, y_km) DEFAULT: None

figsize

Figure size

TYPE: tuple DEFAULT: (16, 12)

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
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
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
def plot_timeseries_displacement_components(
    result: Dict,
    point_km: Optional[Tuple[float, float]] = None,
    figsize: Tuple[float, float] = (16, 12),
) -> plt.Figure:
    """
    Plot displacement components (LOS, Uz, Ue, Un) over time at a point.

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    point_km : tuple (x_km, y_km), optional
        Location. If None, uses max |LOS| location.
    figsize : tuple
        Figure size

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    X_km = result["X_km"]
    Y_km = result["Y_km"]
    meta = result["metadata"]

    n_pre = meta["n_pre"]
    n_event = meta["n_event"]
    n_total = meta["n_total"]

    # Get fields
    LOS_field = result["los_displacement"]
    Ue_field = result["Ue"]
    Un_field = result["Un"]
    Uz_field = result["Uz"]

    # Find point
    if point_km is None:
        max_idx = np.unravel_index(np.argmax(np.abs(LOS_field)), LOS_field.shape)
        iy, ix = max_idx
        x_km = X_km[iy, ix]
        y_km = Y_km[iy, ix]
        point_label = f"Max |LOS| ({x_km:.1f}, {y_km:.1f}) km"
    else:
        x_km, y_km = point_km
        dx = np.abs(X_km[0, :] - x_km)
        dy = np.abs(Y_km[:, 0] - y_km)
        ix = np.argmin(dx)
        iy = np.argmin(dy)
        x_km = X_km[iy, ix]
        y_km = Y_km[iy, ix]
        point_label = f"User-defined ({x_km:.1f}, {y_km:.1f}) km"

    # Extract values
    LOS_val = LOS_field[iy, ix]
    Ue_val = Ue_field[iy, ix]
    Un_val = Un_field[iy, ix]
    Uz_val = Uz_field[iy, ix]
    Uh_val = np.sqrt(Ue_val**2 + Un_val**2)

    # Build time series
    time_indices = np.arange(n_total)
    LOS_ts = np.zeros(n_total)
    Ue_ts = np.zeros(n_total)
    Un_ts = np.zeros(n_total)
    Uz_ts = np.zeros(n_total)
    Uh_ts = np.zeros(n_total)

    for t in range(n_total):
        if n_pre <= t < n_pre + n_event:
            LOS_ts[t] = LOS_val
            Ue_ts[t] = Ue_val
            Un_ts[t] = Un_val
            Uz_ts[t] = Uz_val
            Uh_ts[t] = Uh_val

    # Convert to cm
    LOS_cm = LOS_ts * 100
    Ue_cm = Ue_ts * 100
    Un_cm = Un_ts * 100
    Uz_cm = Uz_ts * 100
    Uh_cm = Uh_ts * 100

    # Create figure
    fig = plt.figure(figsize=figsize)
    gs = fig.add_gridspec(2, 2, height_ratios=[1.2, 1], hspace=0.3, wspace=0.3)

    ax_ts = fig.add_subplot(gs[0, :])
    ax_map = fig.add_subplot(gs[1, 0])
    ax_bar = fig.add_subplot(gs[1, 1])

    # Time series
    ax_ts.axvspan(-0.5, n_pre - 0.5, alpha=0.15, color="blue", label="Pre-event")
    ax_ts.axvspan(n_pre - 0.5, n_pre + n_event - 0.5, alpha=0.25, color="red", label="Event")
    ax_ts.axvspan(n_pre + n_event - 0.5, n_total - 0.5, alpha=0.15, color="green", label="Post-event")

    ax_ts.plot(time_indices, LOS_cm, "ko-", lw=2.5, ms=10, label=f"LOS ({LOS_val*100:.2f} cm)")
    ax_ts.plot(time_indices, Uz_cm, "s-", color="tab:blue", lw=2, ms=8, label=f"Uz ({Uz_val*100:.2f} cm)")
    ax_ts.plot(time_indices, Ue_cm, "^-", color="tab:orange", lw=2, ms=8, label=f"Ue ({Ue_val*100:.2f} cm)")
    ax_ts.plot(time_indices, Un_cm, "v-", color="tab:green", lw=2, ms=8, label=f"Un ({Un_val*100:.2f} cm)")
    ax_ts.plot(time_indices, Uh_cm, "d--", color="tab:purple", lw=2, ms=7, label=f"|Uh| ({Uh_val*100:.2f} cm)")

    ax_ts.axhline(0, color="gray", lw=0.5)
    ax_ts.set_xlabel("Frame")
    ax_ts.set_ylabel("Displacement (cm)")
    ax_ts.set_title(f"Displacement at {point_label}")
    ax_ts.legend(loc="upper right", fontsize=9)
    ax_ts.grid(True, alpha=0.3)
    ax_ts.set_xticks(time_indices)

    # Map
    extent = [X_km.min(), X_km.max(), Y_km.min(), Y_km.max()]
    vmax = np.percentile(np.abs(LOS_field), 99) * 100
    im = ax_map.imshow(LOS_field * 100, extent=extent, origin="lower",
                       cmap="RdBu_r", vmin=-vmax, vmax=vmax)
    plt.colorbar(im, ax=ax_map, label="LOS (cm)")
    ax_map.plot(x_km, y_km, "ko", ms=15, mfc="yellow", mec="black", mew=2)
    ax_map.plot(meta["xcen_km"], meta["ycen_km"], "k*", ms=12)
    ax_map.set_xlabel("X (km)")
    ax_map.set_ylabel("Y (km)")
    ax_map.set_title("LOS Displacement")
    ax_map.set_aspect("equal")

    # Bar chart
    components = ["LOS", "Uz", "Ue", "Un", "|Uh|"]
    values = [LOS_val * 100, Uz_val * 100, Ue_val * 100, Un_val * 100, Uh_val * 100]
    colors_bar = ["black", "tab:blue", "tab:orange", "tab:green", "tab:purple"]
    bars = ax_bar.bar(components, values, color=colors_bar, edgecolor="black", lw=1.5)
    ax_bar.axhline(0, color="gray", lw=0.5)
    ax_bar.set_ylabel("Displacement (cm)")
    ax_bar.set_title("Components at Point")
    ax_bar.grid(True, alpha=0.3, axis="y")

    for bar, val in zip(bars, values):
        h = bar.get_height()
        ax_bar.annotate(f"{val:.2f}", xy=(bar.get_x() + bar.get_width()/2, h),
                       xytext=(0, 3 if h >= 0 else -12), textcoords="offset points",
                       ha="center", va="bottom" if h >= 0 else "top", fontsize=9, fontweight="bold")

    fig.suptitle(f"Displacement Time Series | Mw={meta['Mw']:.1f} | {meta['source_type'].upper()}", fontsize=13)
    plt.tight_layout()
    return fig

plot_timeseries_profile 

plot_timeseries_profile(result: Dict, direction: str = 'EW', position_km: float = 0.0, figsize: Tuple[float, float] = (14, 10)) -> plt.Figure

Plot space-time profile (kymograph).

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

direction

'EW' (East-West) or 'NS' (North-South)

TYPE: str DEFAULT: 'EW'

position_km

Position of profile line in km

TYPE: float DEFAULT: 0.0

figsize

Figure size

TYPE: tuple DEFAULT: (14, 10)

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
def plot_timeseries_profile(
    result: Dict,
    direction: str = "EW",
    position_km: float = 0.0,
    figsize: Tuple[float, float] = (14, 10),
) -> plt.Figure:
    """
    Plot space-time profile (kymograph).

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    direction : str
        'EW' (East-West) or 'NS' (North-South)
    position_km : float
        Position of profile line in km
    figsize : tuple
        Figure size

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    ts = result["timeseries"]
    X_km = result["X_km"]
    Y_km = result["Y_km"]
    meta = result["metadata"]

    n_frames = ts.shape[0]
    n_pre = meta["n_pre"]
    n_event = meta["n_event"]

    if direction.upper() == "EW":
        iy = np.argmin(np.abs(Y_km[:, 0] - position_km))
        profile = ts[:, iy, :]
        distance = X_km[0, :]
        xlabel = "X (km)"
        profile_label = f"Y = {Y_km[iy, 0]:.1f} km"
    else:
        ix = np.argmin(np.abs(X_km[0, :] - position_km))
        profile = ts[:, :, ix]
        distance = Y_km[:, 0]
        xlabel = "Y (km)"
        profile_label = f"X = {X_km[0, ix]:.1f} km"

    fig, axes = plt.subplots(2, 1, figsize=figsize)

    # Kymograph
    extent = [distance.min(), distance.max(), -0.5, n_frames - 0.5]
    im = axes[0].imshow(profile, extent=extent, aspect="auto", origin="lower",
                        cmap="hsv", vmin=-np.pi, vmax=np.pi)
    plt.colorbar(im, ax=axes[0], label="Phase (rad)")
    axes[0].axhline(n_pre - 0.5, color="white", ls="--", lw=2, label="Event start")
    axes[0].axhline(n_pre + n_event - 0.5, color="white", ls=":", lw=2, label="Event end")
    axes[0].set_xlabel(xlabel)
    axes[0].set_ylabel("Frame")
    axes[0].set_title(f"Space-Time Diagram ({profile_label})")
    axes[0].legend(loc="upper right")

    # Profiles
    colors = ["blue", "red", "green"]
    frames = [n_pre // 2, n_pre, n_pre + n_event + (n_frames - n_pre - n_event) // 2]
    labels = [f"Pre (t={frames[0]})", f"Event (t={frames[1]})", f"Post (t={frames[2]})"]

    for frame, label, color in zip(frames, labels, colors):
        axes[1].plot(distance, profile[frame], "-", lw=2, label=label, color=color)

    axes[1].set_xlabel(xlabel)
    axes[1].set_ylabel("Phase (rad)")
    axes[1].set_title("Profiles at Different Times")
    axes[1].legend()
    axes[1].grid(True, alpha=0.3)
    axes[1].set_ylim(-np.pi, np.pi)

    fig.suptitle(f"Space-Time Evolution | Mw={meta['Mw']:.1f} | {direction}", fontsize=12)
    plt.tight_layout()
    return fig

plot_timeseries_difference 

plot_timeseries_difference(result: Dict, n_cols: int = 4, figsize_per_frame: float = 3.5) -> plt.Figure

Plot frame-to-frame differences to highlight deformation onset.

PARAMETER DESCRIPTION
result

Output from generate_timeseries

TYPE: dict

n_cols

Columns in grid

TYPE: int DEFAULT: 4

figsize_per_frame

Size per frame

TYPE: float DEFAULT: 3.5

RETURNS DESCRIPTION
fig

TYPE: Figure

Source code in src/eq_insar/visualization/timeseries.py 
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
def plot_timeseries_difference(
    result: Dict,
    n_cols: int = 4,
    figsize_per_frame: float = 3.5,
) -> plt.Figure:
    """
    Plot frame-to-frame differences to highlight deformation onset.

    Parameters
    ----------
    result : dict
        Output from generate_timeseries
    n_cols : int
        Columns in grid
    figsize_per_frame : float
        Size per frame

    Returns
    -------
    fig : matplotlib.figure.Figure
    """
    ts = result["timeseries"]
    meta = result["metadata"]
    n_frames = ts.shape[0]
    n_pre = meta["n_pre"]
    n_event = meta["n_event"]

    # Compute differences
    diff = np.zeros((n_frames - 1, ts.shape[1], ts.shape[2]))
    for t in range(n_frames - 1):
        diff[t] = np.angle(np.exp(1j * (ts[t + 1] - ts[t])))

    n_diff = n_frames - 1
    n_rows = int(np.ceil(n_diff / n_cols))
    figsize = (figsize_per_frame * n_cols, figsize_per_frame * n_rows)
    fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)
    axes = axes.flatten()

    for i in range(n_diff):
        t_from, t_to = i, i + 1

        if t_to <= n_pre:
            title = f"Pre: {t_from}→{t_to}"
            color = "blue"
        elif t_from < n_pre <= t_to:
            title = f"ONSET: {t_from}→{t_to}"
            color = "red"
        elif t_from < n_pre + n_event <= t_to:
            title = f"END: {t_from}→{t_to}"
            color = "orange"
        elif n_pre <= t_from < n_pre + n_event:
            title = f"Event: {t_from}→{t_to}"
            color = "red"
        else:
            title = f"Post: {t_from}→{t_to}"
            color = "green"

        vmax = max(np.percentile(np.abs(diff[i]), 98), 0.1)
        axes[i].imshow(diff[i], cmap="RdBu_r", vmin=-vmax, vmax=vmax)
        axes[i].set_title(title, fontsize=10, color=color, fontweight="bold")
        axes[i].axis("off")

    for i in range(n_diff, len(axes)):
        axes[i].axis("off")

    fig.suptitle(f"Frame Differences | Mw={meta['Mw']:.1f}", fontsize=12)
    plt.tight_layout()
    return fig