quickdudley/eocalon - Change 3QGP6RXLMNWYWMACHKKYEVECRXP7ESGOSWTEY72O46VLXQR5O6IAC

Automatically format python files

Created by quickdudley on July 11, 2021

3QGP6RXLMNWYWMACHKKYEVECRXP7ESGOSWTEY72O46VLXQR5O6IAC

Dependencies

In channels

main

generic

Change contents

Insertion in trainmodel/src/simsave.py at line 5 [7.20]
[7.85]
[7.85]

Replacement in trainmodel/src/simsave.py at line 8 [7.20]

B:BD[7.103] → [7.103:1084]

B:BD[7.1084] → [2.0:234]

    def __init__(self, stream):
        def check(item):
            if type(item) == dict:
                return {k: check(v) for k, v in item.items()}
            elif type(item) == list:
                return list(map(check, item))
            elif type(item) == str and item.startswith('buffer:'):
                return base64.b64decode(item[len('buffer:'):])
            else:
                return item
        self.raw = check(json.load(stream))
        def vertex(obj):
            return [obj['x'], obj['y'], obj['z']]
        self.vertices = tf.convert_to_tensor(list(map(vertex, self.raw['model']['bodies']['world']['grid']['vertices'])), dtype=tf.float32)
        crust = tf.convert_to_tensor(list(map(lambda x: x[0], struct.iter_unpack('f', self.raw['model']['bodies']['world']['lithosphere']['total_crust']))))
        crust = tf.reshape(crust, (8, self.vertices.shape[0]))
        self.crust_layers = {SimResult.crust_columns()[i]: crust[i] for i in range(0,8)}
        total_crust = tf.zeros((self.vertices.shape[0],), dtype=tf.float32)
        for k, v in self.crust_layers.items():
             if k != 'age':
                 total_crust += v
        self.crust_layers['total'] = total_crust

[7.103]

[7.1131]

  def __init__(self, stream):
    def check(item):
      if type(item) == dict:
        return {k: check(v) for k, v in item.items()}
      elif type(item) == list:
        return list(map(check, item))
      elif type(item) == str and item.startswith('buffer:'):
        return base64.b64decode(item[len('buffer:'):])
      else:
        return item
    self.raw = check(json.load(stream))
    def vertex(obj):
      return [obj['x'], obj['y'], obj['z']]

Replacement in trainmodel/src/simsave.py at line 24 [7.20]

B:BD[7.1132] → [7.1132:1184]

    def seed(self):
        return self.raw['seed']

[7.1132]

[7.1277]

    self.vertices = tf.convert_to_tensor(list(
        map(vertex, self.raw['model']['bodies']['world']['grid']['vertices'])),
                                         dtype=tf.float32)
    crust = tf.convert_to_tensor(
        list(
            map(
                lambda x: x[0],
                struct.iter_unpack(
                    'f', self.raw['model']['bodies']['world']['lithosphere']
                    ['total_crust']))))
    crust = tf.reshape(crust, (8, self.vertices.shape[0]))
    self.crust_layers = {
        SimResult.crust_columns()[i]: crust[i]
        for i in range(0, 8)
    }
    total_crust = tf.zeros((self.vertices.shape[0], ), dtype=tf.float32)
    for k, v in self.crust_layers.items():
      if k != 'age':
        total_crust += v
    self.crust_layers['total'] = total_crust

Replacement in trainmodel/src/simsave.py at line 45 [7.20]

B:BD[7.1278] → [7.1278:1435]

    def crust_columns():
        return ['sediment','sedimentary','metamorphic','felsic_plutonic','felsic_volcanic','mafic_volcanic','mafic_plutonic','age']

[7.1278]

[7.1435]

  def seed(self):
    return self.raw['seed']

Insertion in trainmodel/src/simsave.py at line 48 [7.20]

[7.1436]

  def crust_columns():
    return [
        'sediment', 'sedimentary', 'metamorphic', 'felsic_plutonic',
        'felsic_volcanic', 'mafic_volcanic', 'mafic_plutonic', 'age'
    ]

Replacement in trainmodel/src/nativehelpers.py at line 6 [8.158]

B:BD[8.212] → [8.212:359]

native = ctypes.cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/helpers.' + ('dylib' if platform.system() == 'Darwin' else 'so'))

[8.212]

[9.393]

native = ctypes.cdll.LoadLibrary(
    os.path.dirname(os.path.realpath(__file__)) + '/helpers.' +
    ('dylib' if platform.system() == 'Darwin' else 'so'))

Replacement in trainmodel/src/nativehelpers.py at line 10 [8.158]

∅:D[9.394] → [8.359:451]

B:BD[8.359] → [8.359:451]

native.render_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int]

[9.394]

[8.451]

native.render_tensor.argtypes = [
    ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int
]

Replacement in trainmodel/src/nativehelpers.py at line 16 [8.158]

B:BD[8.562] → [9.395:491]

native.equirectangular.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int]

[8.562]

[9.491]

native.equirectangular.argtypes = [
    ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int
]

Replacement in trainmodel/src/nativehelpers.py at line 21 [8.158]

B:BD[10.1] → [10.1:134]

native.colourize_heightmap.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int]

[10.1]

[10.134]

native.colourize_heightmap.argtypes = [
    ctypes.POINTER(ctypes.c_double),
    ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int
]

Replacement in trainmodel/src/nativehelpers.py at line 27 [8.158]

B:BD[11.1] → [11.1:129]

native.landmass_steradians.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]

[11.1]

[11.129]

native.landmass_steradians.argtypes = [
    ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int, ctypes.c_int,
    ctypes.c_int
]

Insertion in trainmodel/src/model.py at line 5 [14.1123]
[12.22]
[13.0]

Replacement in trainmodel/src/model.py at line 8 [14.1123]

B:BD[13.26] → [3.0:402]

∅:D[3.402] → [15.0:349]

B:BD[13.376] → [15.0:349]

B:BD[15.349] → [3.403:1055]

∅:D[3.1055] → [13.1312:1367]

B:BD[13.1312] → [13.1312:1367]

    layer1 = keras.layers.Dense(20, activation=keras.activations.softplus)(inputs)
    layer2 = keras.layers.Dense(20, activation=keras.activations.softplus)(layer1)
    layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))
    layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))
    layer5 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer2,layer3]))
    layer6 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer2,layer4,layer5]))
    layer7 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer5,layer6]))
    layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer3,layer5,layer7]))
    layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer4,layer6,layer7]))
    layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer7,layer8,layer9]))
    layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer3,layer8,layer10]))
    layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer4,layer9,layer10,layer11]))
    return keras.layers.concatenate([layer11,layer12])

[13.26]

[14.1194]

  layer1 = keras.layers.Dense(20,
                              activation=keras.activations.softplus)(inputs)
  layer2 = keras.layers.Dense(20,
                              activation=keras.activations.softplus)(layer1)
  layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer1, layer2]))
  layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer1, layer2]))
  layer5 = keras.layers.Dense(88, activation=keras.activations.relu)(
      keras.layers.concatenate([layer2, layer3]))
  layer6 = keras.layers.Dense(88, activation=keras.activations.relu)(
      keras.layers.concatenate([layer2, layer4, layer5]))
  layer7 = keras.layers.Dense(88, activation=keras.activations.relu)(
      keras.layers.concatenate([layer5, layer6]))
  layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer3, layer5, layer7]))
  layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer4, layer6, layer7]))
  layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer7, layer8, layer9]))
  layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer1, layer3, layer8, layer10]))
  layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(
      keras.layers.concatenate([layer1, layer4, layer9, layer10, layer11]))
  return keras.layers.concatenate([layer11, layer12])

Insertion in trainmodel/src/model.py at line 34 [14.1123]
[14.1195]
[14.1195]

Replacement in trainmodel/src/model.py at line 36 [14.1123]

B:BD[14.1208] → [14.1208:1245]

B:BD[14.1245] → [13.1368:1535]

∅:D[13.1535] → [14.1668:1745]

B:BD[14.1668] → [14.1668:1745]

    inputs = keras.Input(shape=(3,))
    d1 = icosahedral(inputs)
    d2 = icosahedral(inputs)
    outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(keras.layers.concatenate([d1,d2]))
    return keras.Model(inputs=inputs, outputs=outputs, name="geology_model")

[14.1208]

[14.1745]

  inputs = keras.Input(shape=(3, ))
  d1 = icosahedral(inputs)
  d2 = icosahedral(inputs)
  outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(
      keras.layers.concatenate([d1, d2]))
  return keras.Model(inputs=inputs, outputs=outputs, name="geology_model")

Replacement in trainmodel/src/model.py at line 45 [14.1123]

B:BD[16.40] → [5.0:106]

∅:D[5.106] → [16.130:185]

∅:D[10.266] → [16.130:185]

B:BD[16.130] → [16.130:185]

    y_true = tf.math.asinh((y_true - 0.1) * 5.) / 5.
    y_pred = tf.math.asinh((y_pred - 0.1) * 5.) / 5.
    return tf.math.reduce_mean((y_true - y_pred) ** 2)

[16.40]

[16.185]

  y_true = tf.math.asinh((y_true - 0.1) * 5.) / 5.
  y_pred = tf.math.asinh((y_pred - 0.1) * 5.) / 5.
  return tf.math.reduce_mean((y_true - y_pred)**2)

Replacement in trainmodel/src/model.py at line 51 [14.1123]

B:BD[14.1776] → [12.23:114]

    return (simresult.vertices, (geology.isostatic_displacement(simresult) + 1.) / 25000.)

[14.1776]

[14.1872]

  return (simresult.vertices,
          (geology.isostatic_displacement(simresult) + 1.) / 25000.)

Replacement in trainmodel/src/model.py at line 56 [14.1123]

B:BD[14.1901] → [14.1901:2054]

    custom_objects = {"asinh": tf.math.asinh}
    with keras.utils.custom_object_scope(custom_objects):
        return keras.models.clone_model(source)

[14.1901]

  custom_objects = {"asinh": tf.math.asinh}
  with keras.utils.custom_object_scope(custom_objects):
    return keras.models.clone_model(source)

Replacement in trainmodel/src/geology.py at line 11 [12.150]

B:BD[12.406] → [12.406:828]

        'fine_sediment': 1500.,
        'voarse_sediment': 1500.,
        'sediment': 1500.,
        'sedimentary': 2600.,
        'metamorphic': 2800.,
        'felsic_plutonic': 2600.,
        'felsic_volcanic': 2600.,
        'mafic_volcanic_min': 2890., # Carlson & Raskin 1984
        'mafic_volcanic_max': 3300.,
        'mantle': 3075., # derived empirically using isostatic model
        'ocean': 1026.,
        }

[12.406]

[12.828]

    'fine_sediment': 1500.,
    'voarse_sediment': 1500.,
    'sediment': 1500.,
    'sedimentary': 2600.,
    'metamorphic': 2800.,
    'felsic_plutonic': 2600.,
    'felsic_volcanic': 2600.,
    'mafic_volcanic_min': 2890.,  # Carlson & Raskin 1984
    'mafic_volcanic_max': 3300.,
    'mantle': 3075.,  # derived empirically using isostatic model
    'ocean': 1026.,
}

Replacement in trainmodel/src/geology.py at line 26 [12.150]

B:BD[12.855] → [12.855:1406]

    fraction_of_lifetime = saved.crust_layers['age'] / 7889537440886400.0
    mafic_density = material_density['mafic_volcanic_min'] + (material_density['mafic_volcanic_max'] - material_density['mafic_volcanic_min']) * fraction_of_lifetime
    total_thickness = (saved.crust_layers['mafic_volcanic'] + saved.crust_layers['mafic_plutonic']) / mafic_density
    for rocktype, density in material_density.items():
        if rocktype in saved.crust_layers:
            total_thickness += saved.crust_layers[rocktype] / density
    return total_thickness

[12.855]

[12.1406]

  fraction_of_lifetime = saved.crust_layers['age'] / 7889537440886400.0
  mafic_density = material_density['mafic_volcanic_min'] + (
      material_density['mafic_volcanic_max'] -
      material_density['mafic_volcanic_min']) * fraction_of_lifetime
  total_thickness = (saved.crust_layers['mafic_volcanic'] +
                     saved.crust_layers['mafic_plutonic']) / mafic_density
  for rocktype, density in material_density.items():
    if rocktype in saved.crust_layers:
      total_thickness += saved.crust_layers[rocktype] / density
  return total_thickness

Replacement in trainmodel/src/geology.py at line 39 [12.150]

B:BD[12.1442] → [12.1442:1559]

    thickness = get_thickness(saved)
    return thickness - saved.crust_layers['total'] / material_density['mantle']

[12.1442]

  thickness = get_thickness(saved)
  return thickness - saved.crust_layers['total'] / material_density['mantle']

Replacement in trainmodel/src/drawmap.py at line 16 [18.13]

∅:D[17.12] → [18.98:201]

∅:D[14.553] → [18.98:201]

B:BD[18.98] → [18.98:201]

  window = sdl2.ext.Window("Training neural network: memorizing fictional world map", size=(1024,512))

[17.12]

[18.202]

  window = sdl2.ext.Window(
      "Training neural network: memorizing fictional world map",
      size=(1024, 512))

Replacement in trainmodel/src/drawmap.py at line 27 [18.13]

B:BD[18.252] → [19.34:82]

B:BD[19.82] → [17.13:41]

B:BD[17.41] → [11.1723:1853]

∅:D[11.1853] → [19.262:402]

B:BD[19.262] → [19.262:402]

∅:D[19.402] → [18.252:408]

∅:D[14.864] → [18.252:408]

B:BD[18.252] → [18.252:408]

B:BD[18.408] → [20.0:53]

B:BD[20.53] → [21.23:106]

B:BD[21.106] → [11.1854:1969]

B:BD[11.1969] → [6.77:226]

∅:D[6.226] → [21.106:320]

∅:D[11.2093] → [21.106:320]

B:BD[21.106] → [21.106:320]

B:BD[21.320] → [6.227:325]

∅:D[6.325] → [21.397:459]

B:BD[21.397] → [21.397:459]

∅:D[20.135] → [18.408:431]

∅:D[21.459] → [18.408:431]

B:BD[18.408] → [18.408:431]

      if time.monotonic() - last_rendered > 15:
        outputs = m(inputs)
        image = tensor_to_surface(tf.math.multiply(255, colourize_heightmap(tf.reshape(outputs, (512, 1024, outputs.shape[1])))))
        sdl2.SDL_BlitSurface(image, None, windowsurface, None)
        last_rendered = time.monotonic()
        sdl2.SDL_FreeSurface(image)
      events = sdl2.ext.get_events()
      for event in events:
          if event.type == sdl2.SDL_QUIT:
              running = False
              break
          elif event.type == sdl2.SDL_MOUSEBUTTONUP:
              point = list(inputs[event.button.y * 1024 + event.button.x].numpy())
              solid_angle = landmass_steradians(tf.reshape(outputs, (512,1024,1)), event.button.x, event.button.y)
              print('Clicked land mass: %s square Km | %s square miles' % (solid_angle * planet_radius_km**2, solid_angle * planet_radius_miles**2))
              if last_clicked != None:
                  dug = math.sqrt(sum(map((lambda p: (p[0]-p[1])**2), zip(last_clicked, point))))
                  arc = math.acos(1 - dug**2 / 2) # cosine rule, a == b == 1
                  print('%s Km | %s miles' % (arc * planet_radius_km, arc * planet_radius_miles))
              print(point)
              last_clicked = point
      window.refresh()

[18.252]

[18.431]

    if time.monotonic() - last_rendered > 15:
      outputs = m(inputs)
      image = tensor_to_surface(
          tf.math.multiply(
              255,
              colourize_heightmap(
                  tf.reshape(outputs, (512, 1024, outputs.shape[1])))))
      sdl2.SDL_BlitSurface(image, None, windowsurface, None)
      last_rendered = time.monotonic()
      sdl2.SDL_FreeSurface(image)
    events = sdl2.ext.get_events()
    for event in events:
      if event.type == sdl2.SDL_QUIT:
        running = False
        break
      elif event.type == sdl2.SDL_MOUSEBUTTONUP:
        point = list(inputs[event.button.y * 1024 + event.button.x].numpy())
        solid_angle = landmass_steradians(tf.reshape(outputs, (512, 1024, 1)),
                                          event.button.x, event.button.y)
        print('Clicked land mass: %s square Km | %s square miles' %
              (solid_angle * planet_radius_km**2,
               solid_angle * planet_radius_miles**2))
        if last_clicked != None:
          dug = math.sqrt(
              sum(map((lambda p: (p[0] - p[1])**2), zip(last_clicked, point))))
          arc = math.acos(1 - dug**2 / 2)  # cosine rule, a == b == 1
          print('%s Km | %s miles' %
                (arc * planet_radius_km, arc * planet_radius_miles))
        print(point)
        last_clicked = point
    window.refresh()

Insertion in trainmodel/src/drawmap.py at line 60 [18.13]
[22.278]
[9.120]

Replacement in trainmodel/src/drawmap.py at line 62 [18.13]

B:BD[9.163] → [9.163:392]

    n = numpy.zeros((height, width, 3), numpy.float64)
    helpers.equirectangular(ctypes.cast(ctypes.c_voidp(n.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(width), ctypes.c_int(height))
    return tf.constant(n)

[9.163]

[8.90]

  n = numpy.zeros((height, width, 3), numpy.float64)
  helpers.equirectangular(
      ctypes.cast(ctypes.c_voidp(n.ctypes.data),
                  ctypes.POINTER(ctypes.c_double)), ctypes.c_int(width),
      ctypes.c_int(height))
  return tf.constant(n)

Insertion in trainmodel/src/drawmap.py at line 69 [18.13]
[8.91]
[22.460]

Replacement in trainmodel/src/drawmap.py at line 71 [18.13]

B:BD[22.491] → [22.491:516]

B:BD[22.516] → [14.900:1089]

    shape = source.shape
    source = tf.cast(source, tf.uint8).numpy()
    return helpers.render_tensor(ctypes.c_void_p(source.ctypes.data), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(shape[2]))

[22.491]

[23.913]

  shape = source.shape
  source = tf.cast(source, tf.uint8).numpy()
  return helpers.render_tensor(ctypes.c_void_p(source.ctypes.data),
                               ctypes.c_int(shape[1]), ctypes.c_int(shape[0]),
                               ctypes.c_int(shape[2]))

Insertion in trainmodel/src/drawmap.py at line 77 [18.13]
[23.914]
[10.1478]

Replacement in trainmodel/src/drawmap.py at line 79 [18.13]

B:BD[10.1511] → [10.1511:1908]

    shape = source.shape
    n = numpy.zeros((shape[0],shape[1],3), numpy.float64)
    source = tf.cast(source, tf.float64).numpy()
    helpers.colourize_heightmap(ctypes.cast(ctypes.c_voidp(n.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.cast(ctypes.c_voidp(source.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]))
    return tf.constant(n)

[10.1511]

[11.2094]

  shape = source.shape
  n = numpy.zeros((shape[0], shape[1], 3), numpy.float64)
  source = tf.cast(source, tf.float64).numpy()
  helpers.colourize_heightmap(
      ctypes.cast(ctypes.c_voidp(n.ctypes.data),
                  ctypes.POINTER(ctypes.c_double)),
      ctypes.cast(ctypes.c_voidp(source.ctypes.data),
                  ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]),
      ctypes.c_int(shape[0]))
  return tf.constant(n)

Replacement in trainmodel/src/drawmap.py at line 92 [18.13]

B:BD[11.2134] → [11.2134:2411]

    shape = source.shape
    source = tf.cast(source, tf.float64).numpy()
    return helpers.landmass_steradians(ctypes.cast(ctypes.c_voidp(source.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(x), ctypes.c_int(y))

[11.2134]

  shape = source.shape
  source = tf.cast(source, tf.float64).numpy()
  return helpers.landmass_steradians(
      ctypes.cast(ctypes.c_voidp(source.ctypes.data),
                  ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]),
      ctypes.c_int(shape[0]), ctypes.c_int(x), ctypes.c_int(y))

Insertion in trainmodel/src/__main__.py at line 9 [26.14]
[24.44]
[25.29]

Replacement in trainmodel/src/main.py at line 12 [26.14]

∅:D[17.66] → [25.51:103]

∅:D[14.80] → [25.51:103]

B:BD[25.51] → [25.51:103]

B:BD[25.103] → [14.81:129]

B:BD[14.129] → [17.67:146]

B:BD[17.146] → [4.29:83]

    source = simsave.SimResult(open(filename, 'r'))
    training_data = model.training_data(source)
    m.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
    tfjs.converters.save_keras_model(m, 'tfjs_model')

[17.66]

[25.128]

  source = simsave.SimResult(open(filename, 'r'))
  training_data = model.training_data(source)
  m.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
  tfjs.converters.save_keras_model(m, 'tfjs_model')

Replacement in trainmodel/src/main.py at line 17 [26.14]

B:BD[25.129] → [27.25:110]

B:BD[27.110] → [17.147:199]

B:BD[17.199] → [10.1909:1985]

∅:D[28.156] → [27.222:335]

∅:D[10.1985] → [27.222:335]

B:BD[27.222] → [27.222:335]

    inputs = tf.reshape(drawmap.inputs_equirectangular(2048,1024), (2048 * 1024, 3))
    outputs = tf.reshape(m(inputs), (1024,2048, 1))
    outputs = tf.cast(drawmap.colourize_heightmap(outputs) * 255, tf.uint8)
    outputs = tf.io.encode_png(outputs).numpy()
    f = open("map.png", 'wb')
    f.write(outputs)
    f.close()

[25.129]

[27.335]

  inputs = tf.reshape(drawmap.inputs_equirectangular(2048, 1024),
                      (2048 * 1024, 3))
  outputs = tf.reshape(m(inputs), (1024, 2048, 1))
  outputs = tf.cast(drawmap.colourize_heightmap(outputs) * 255, tf.uint8)
  outputs = tf.io.encode_png(outputs).numpy()
  f = open("map.png", 'wb')
  f.write(outputs)
  f.close()

Replacement in trainmodel/src/main.py at line 28 [26.14]

B:BD[25.151] → [14.240:327]

B:BD[14.327] → [16.187:229]

∅:D[16.229] → [14.376:390]

B:BD[14.376] → [14.376:390]

B:BD[14.390] → [17.200:281]

∅:D[17.281] → [25.230:252]

∅:D[14.508] → [25.230:252]

B:BD[25.230] → [25.230:252]

B:BD[25.252] → [17.282:301]

∅:D[17.301] → [25.270:291]

∅:D[14.534] → [25.270:291]

B:BD[25.270] → [25.270:291]

    m = model.model()
    m.compile(
            optimizer=keras.optimizers.RMSprop(),
            loss=model.shore_focused_loss
            )
    bg_thread = threading.Thread(target=train, args=(m, sys.argv[1]), kwargs={})
    bg_thread.start()
    drawmap.run(m)
    bg_thread.join()

[25.151]

[25.291]

  m = model.model()
  m.compile(optimizer=keras.optimizers.RMSprop(),
            loss=model.shore_focused_loss)
  bg_thread = threading.Thread(target=train, args=(m, sys.argv[1]), kwargs={})
  bg_thread.start()
  drawmap.run(m)
  bg_thread.join()

Replacement in trainmodel/src/__main__.py at line 36 [26.14]
B:BD[25.297] → [25.297:366]
∅:D[24.44] → [26.45:46]
∅:D[25.366] → [26.45:46]
B:BD[26.45] → [26.45:46]
```
    print("Expecting filename of tectonic.js save", file=sys.stderr)
```
[25.297]
```
  print("Expecting filename of tectonic.js save", file=sys.stderr)
```

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Insertion in trainmodel/src/simsave.py at line 5 [7.20]

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Replacement in trainmodel/src/geology.py at line 11 [12.150]

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Replacement in trainmodel/src/geology.py at line 39 [12.150]

Replacement in trainmodel/src/drawmap.py at line 16 [18.13]

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Insertion in trainmodel/src/drawmap.py at line 60 [18.13]

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Replacement in trainmodel/src/drawmap.py at line 92 [18.13]

Insertion in trainmodel/src/__main__.py at line 9 [26.14]

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Replacement in trainmodel/src/__main__.py at line 17 [26.14]

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Insertion in trainmodel/src/main.py at line 9 [26.14]

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Replacement in trainmodel/src/main.py at line 36 [26.14]