我们从Python开源项目中，提取了以下41个代码示例，用于说明如何使用numpy.around()。
def get_labels(contours, shape, slices): z = [np.around(s.ImagePositionPatient[2], 1) for s in slices] pos_r = slices[0].ImagePositionPatient[1] spacing_r = slices[0].PixelSpacing[1] pos_c = slices[0].ImagePositionPatient[0] spacing_c = slices[0].PixelSpacing[0] label_map = np.zeros(shape, dtype=np.float32) for con in contours: num = ROI_ORDER.index(con['name']) + 1 for c in con['contours']: nodes = np.array(c).reshape((-1, 3)) assert np.amax(np.abs(np.diff(nodes[:, 2]))) == 0 z_index = z.index(np.around(nodes[0, 2], 1)) r = (nodes[:, 1] - pos_r) / spacing_r c = (nodes[:, 0] - pos_c) / spacing_c rr, cc = polygon(r, c) label_map[z_index, rr, cc] = num return label_map
def ani_update(arg, ii=[0]): i = ii[0] # don't ask... if np.isclose(t_arr[i], np.around(t_arr[i], 1)): fig2.suptitle('Evolution (Time: {})'.format(t_arr[i]), fontsize=24) graphic_floor[0].set_data([-floor_lim*np.cos(incline_history[i]) + radius*np.sin(incline_history[i]), floor_lim*np.cos(incline_history[i]) + radius*np.sin(incline_history[i])], [-floor_lim*np.sin(incline_history[i])-radius*np.cos(incline_history[i]), floor_lim*np.sin(incline_history[i])-radius*np.cos(incline_history[i])]) graphic_wheel.center = (x_history[i], y_history[i]) graphic_ind[0].set_data([x_history[i], x_history[i] + radius*np.sin(w_history[i])], [y_history[i], y_history[i] + radius*np.cos(w_history[i])]) graphic_pend[0].set_data([x_history[i], x_history[i] - cw_to_cm[1]*np.sin(q_history[i, 2])], [y_history[i], y_history[i] + cw_to_cm[1]*np.cos(q_history[i, 2])]) graphic_dist[0].set_data([x_history[i] - cw_to_cm[1]*np.sin(q_history[i, 2]), x_history[i] - cw_to_cm[1]*np.sin(q_history[i, 2]) - pscale*p_history[i]*np.cos(q_history[i, 2])], [y_history[i] + cw_to_cm[1]*np.cos(q_history[i, 2]), y_history[i] + cw_to_cm[1]*np.cos(q_history[i, 2]) - pscale*p_history[i]*np.sin(q_history[i, 2])]) ii[0] += int(1 / (timestep * framerate)) if ii[0] >= len(t_arr): print("Resetting animation!") ii[0] = 0 return [graphic_floor, graphic_wheel, graphic_ind, graphic_pend, graphic_dist] # Run animation
def gen_adj_mat(longs, lats, prob_edge=.2, additional_length=lambda: np.random.exponential(20,1)): '''Get an adjacency matrix for the cities whose longitudes and latitudes are passed. Each entry will either be a number somewhat greater than the crow-flies distance between the two cities (with probability prob_edge), or math.inf. The matrix will consist of floats, and be symmetric. The diagonal will be all zeroes. The "somewhat greater" is controlled by the additional_length parameter, a function returning a random amount.''' # Generate full nxn Bernoulli's, even though we'll only use the upper # triangle. edges = np.random.binomial(1, prob_edge, size=(len(longs),len(longs))) am = np.zeros((len(longs),len(longs))) for i in range(len(longs)): for j in range(len(longs)): if i==j: am[i,i] = 0 elif i < j: if edges[i,j] == 1: am[i,j] = (math.hypot(longs[i]-longs[j],lats[i]-lats[j]) + additional_length()) am[j,i] = am[i,j] else: am[i,j] = am[j,i] = math.inf return np.around(am,1)
def bbox_indices(self, bbox, shape, precision=7): """ Return row and column coordinates of a bounding box at a given cellsize. Parameters ---------- bbox : tuple of floats or ints (length 4) bbox of new data. shape : tuple of ints (length 2) The shape of the 2D array (rows, columns). precision : int Precision to use when matching geographic coordinates. """ rows = np.around(np.linspace(bbox[1], bbox[3], shape[0], endpoint=False)[::-1], precision) cols = np.around(np.linspace(bbox[0], bbox[2], shape[1], endpoint=False), precision) return rows, cols
def round_(a, decimals=0, out=None): """ Round an array to the given number of decimals. Refer to `around` for full documentation. See Also -------- around : equivalent function """ try: round = a.round except AttributeError: return _wrapit(a, 'round', decimals, out) return round(decimals, out)
def get_cell(self): # from fractions import Fraction marr = np.array(self._matrix, dtype=np.float64).reshape((3, 3)) g_arr = self._sites_grid.to_array() d = self.depth w = self.width l = self.length arr_bas = marr*np.array([d, w, l], dtype=np.int).reshape((3, 1)) grid_position = np.array([p for p in CStru._yield_position(d, w, l)]) frac = np.array([1/d, 1/w, 1/l], dtype=np.float64).reshape((1, 3)) # round_frac = np.around(frac, decimals=22) arr_pos = grid_position * frac arr_num = np.array([i for i in g_arr.flat]) return (arr_bas, arr_pos, arr_num)
def _get_new_id_seq(pos, numbers): """ A helper function to produce the new sequence of the transformed structure. Algs is sort the position back to init and use the index to sort numbers. """ # transfer the atom position into >=0 and <=1 pos = np.around(pos, decimals=3) func_tofrac = np.vectorize(lambda x: round((x % 1), 3)) o_pos = func_tofrac(pos) # round_o_pos = np.around(o_pos, decimals=3) # z, y, x = round_o_pos[:, 2], round_o_pos[:, 1], round_o_pos[:, 0] z, y, x = o_pos[:, 2], o_pos[:, 1], o_pos[:, 0] inds = np.lexsort((z, y, x)) return inds
def _get_new_id_seq(pos, numbers): """ A helper function to produce the new sequence of the transformed structure. Algs is sort the position back to init and use the index to sort numbers. """ # transfer the atom position into >=0 and <=1 pos = np.around(pos, decimals=5) func_tofrac = np.vectorize(lambda x: round((x % 1), 3)) o_pos = func_tofrac(pos) # round_o_pos = np.around(o_pos, decimals=3) # z, y, x = round_o_pos[:, 2], round_o_pos[:, 1], round_o_pos[:, 0] z, y, x = o_pos[:, 2], o_pos[:, 1], o_pos[:, 0] inds = np.lexsort((z, y, x)) return inds
def __init__(self, gcell): self.lattice = np.around(gcell.lattice, decimals=6) self.positions = np.around(gcell.positions, decimals=6) self.numbers = gcell.numbers atoms_name_list = list(map(lambda x: Specie.to_name(x), list(self.numbers))) d = Counter(atoms_name_list) ordered_atoms = OrderedDict(sorted(d.items(), key=lambda x: Specie(x[0]).Z)) # remove Ghostatoms if 'G' in ordered_atoms: del ordered_atoms['G'] self.comment = ''.join(['{}{}'.format(k, v) for k, v in ordered_atoms.items()])
def _pnl_pos(self, e, s, a, pnl, inputs): ''' Return the reward based on PnL from the last step marked to the mid-price of the instruments traded :param e: Environment object. Environment where the agent operates :param a: Agent object. the agent that will perform the action :param s: dictionary. The inputs from environment to the agent :param pnl: float. The current pnl of the agent :param inputs: dictionary. The inputs from environment to the agent ''' reward = self._pnl(e, s, a, pnl, inputs) s_main = e.s_main_intrument if not a.logged_action: return reward f_penalty = abs(e.agent_states[a][s_main]['Position']) * 0.02 f_penalty += abs(np.around(a.log_info['duration'])) * 0.30 return reward - f_penalty
def fixOffset(self, offset, img): size = img.shape finalImg = np.ndarray(size) indices = np.indices((self.videoSize[0],self.videoSize[1])).swapaxes(0,2).swapaxes(0,1) indices = np.around(indices, decimals=1) indices.shape = (self.videoSize[1] * self.videoSize[0], 2) phi = 2 * np.arctan(np.exp(indices[:, 1] / self.videoSize[1])) - 1/2 * np.pi - offset[0] lamb = indices[:, 0] - offset[1] x = lamb y = np.log(np.tan(np.pi / 4 + 1/2 * phi)) * self.videoSize[1] finalIdx = np.ndarray((self.videoSize[1] * self.videoSize[0], 2)) finalIdx = np.around(finalIdx, decimals=1).astype(int) finalIdx[:, 1] = y % self.videoSize[1] finalIdx[:, 0] = x % self.videoSize[0] finalImg[indices[:,1], indices[:,0]] = img[finalIdx[:,1], finalIdx[:,0]] return finalImg
def imresizemex(inimg, weights, indices, dim): in_shape = inimg.shape w_shape = weights.shape out_shape = list(in_shape) out_shape[dim] = w_shape[0] outimg = np.zeros(out_shape) if dim == 0: for i_img in range(in_shape[1]): for i_w in range(w_shape[0]): w = weights[i_w, :] ind = indices[i_w, :] im_slice = inimg[ind, i_img].astype(np.float64) outimg[i_w, i_img] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0) elif dim == 1: for i_img in range(in_shape[0]): for i_w in range(w_shape[0]): w = weights[i_w, :] ind = indices[i_w, :] im_slice = inimg[i_img, ind].astype(np.float64) outimg[i_img, i_w] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0) if inimg.dtype == np.uint8: outimg = np.clip(outimg, 0, 255) return np.around(outimg).astype(np.uint8) else: return outimg
def frac_coord(npixel, kernel_oversampling, p): """ Compute whole and fractional parts of coordinates, rounded to kernel_oversampling-th fraction of pixel size The fractional values are rounded to nearest 1/kernel_oversampling pixel value. At fractional values greater than (kernel_oversampling-0.5)/kernel_oversampling coordinates are rounded to next integer index. :param npixel: Number of pixels in total :param kernel_oversampling: Fractional values to round to :param p: Coordinate in range [-.5,.5[ """ assert numpy.array(p >= -0.5).all() and numpy.array( p < 0.5).all(), "Cellsize is too large: uv overflows grid uv= %s" % str(p) x = npixel // 2 + p * npixel flx = numpy.floor(x + 0.5 / kernel_oversampling) fracx = numpy.around((x - flx) * kernel_oversampling) return flx.astype(int), fracx.astype(int)
def test_optimized(self): """ Test if optimized works well """ lr = self.logistic_regression lr.set_data(self.iris) op_theta = lr.optimized() self.assertEqual(len(op_theta), 4) # check if really minimal, function is monotonic so everywhere around # j should be higher self.assertLessEqual( lr.j(op_theta), lr.j(op_theta + np.array([1, 0, 0, 0]))) self.assertLessEqual( lr.j(op_theta), lr.j(op_theta + np.array([0, 1, 0, 0]))) self.assertLessEqual( lr.j(op_theta), lr.j(op_theta + np.array([0, 0, 1, 0]))) self.assertLessEqual( lr.j(op_theta), lr.j(op_theta + np.array([0, 0, 0, 1])))
def ani_update(arg, ii=[0]): i = ii[0] # don't ask... if np.isclose(t_arr[i], np.around(t_arr[i], 1)): fig2.suptitle('Evolution (Time: {})'.format(t_arr[i]), fontsize=24) graphic_robot_1.center = ((x_history[i, 0]+td*np.cos(x_history[i, 2]), x_history[i, 1]+td*np.sin(x_history[i, 2]))) graphic_robot_2.center = ((x_history[i, 0], x_history[i, 1])) graphic_robot_3.center = ((x_history[i, 0]-td*np.cos(x_history[i, 2]), x_history[i, 1]-td*np.sin(x_history[i, 2]))) ii[0] += int(1 / (dt * framerate)) if ii[0] >= len(t_arr): print("Resetting animation!") ii[0] = 0 return None # Run animation
def update(arg, ii=[0]): i = ii[0] # don't ask... if np.isclose(t_arr[i], np.around(t_arr[i], 1)): fig3.suptitle('Evolution (Time: {})'.format(t_arr[i]), fontsize=24) link1[0].set_data([0, elb_history[i, 0]], [0, elb_history[i, 1]]) link2[0].set_data([elb_history[i, 0], x_history[i, 0]], [elb_history[i, 1], x_history[i, 1]]) end.set_offsets((x_history[i, 0], x_history[i, 1])) elb.set_offsets((elb_history[i, 0], elb_history[i, 1])) ii[0] += int(1 / (dt * framerate)) if ii[0] >= len(t_arr): print("Resetting animation!") ii[0] = 0 return [link1, link2, end, elb] # Run animation
def test_encode_texts(): """ Text encoding is stable. """ TEST_SENTENCES = [u'I love mom\'s cooking', u'I love how you never reply back..', u'I love cruising with my homies', u'I love messing with yo mind!!', u'I love you and now you\'re just gone..', u'This is shit', u'This is the shit'] maxlen = 30 batch_size = 32 with open(VOCAB_PATH, 'r') as f: vocabulary = json.load(f) st = SentenceTokenizer(vocabulary, maxlen) tokenized, _, _ = st.tokenize_sentences(TEST_SENTENCES) model = deepmoji_feature_encoding(maxlen, PRETRAINED_PATH) encoding = model.predict(tokenized) avg_across_sentences = np.around(np.mean(encoding, axis=0)[:5], 3) assert np.allclose(avg_across_sentences, np.array([-0.023, 0.021, -0.037, -0.001, -0.005]))
def myImputer(kernel,sample): kernelSize = kernel.shape[0]; vectorSize = sample.shape[3]; for fIndex in range(0,int(sample.shape[0])): for sIndex in range(0,int(sample.shape[1])): sslice = sample[fIndex][sIndex] #find the index of -1 [x,y] = np.where(sslice == -1) #change -1 to 0 sslice[sslice == -1] = 0 if x.size == 0: continue #broaden the vector tempVectorH = np.zeros([int((kernelSize-1)/2),vectorSize]) tempVectorV = np.zeros([vectorSize-1+kernelSize,int((kernelSize-1)/2)]) tempSlice = np.vstack((tempVectorH,sslice,tempVectorH)) tempSlice = np.hstack((tempVectorV,tempSlice,tempVectorV)) zeroSlice = np.zeros(sslice.shape); for k in range(len(x)): subSlice = tempSlice[x[k]:x[k]+kernelSize,y[k]:y[k]+kernelSize] imputerValue = np.sum(subSlice*kernel) zeroSlice[x[k],y[k]] = np.around(imputerValue) sslice += zeroSlice.astype("int32")
def prepare_basemap(min_lat, min_lon, max_lat, max_lon, delta_lat, delta_lon): """ :param min_lat: float :param min_lon: float :param max_lat: float :param max_lon: float :param delta_lat: float :param delta_lon: float :return: A Basemap instance """ m = Basemap(projection='cyl', llcrnrlat=min_lat, urcrnrlat=max_lat, urcrnrlon=max_lon, llcrnrlon=min_lon, resolution='l') m.drawcountries() m.drawcoastlines() m.drawparallels(np.arange(np.around(min_lat, 1), np.around(max_lat, 1), -round(delta_lat) / 5.0), labels=[1, 0, 0, 0]) m.drawmeridians(np.arange(np.around(min_lon, 1), np.around(max_lon, 1), -round(delta_lon) / 5.0), labels=[0, 0, 0, 1]) return m
def svimg(totarr): #print it out: x,y=totarr.shape vl = np.around(totarr.flatten(),5)#round to 5 digits xx = np.repeat(np.arange(x),x)+1 yy = np.tile(np.arange(y),y)+1 big =np.column_stack((xx,yy,vl)) np.savetxt("noisyimage.txt",big,fmt=('%4.1f','%4.1f','%10.5f')) ##Add this if you want to ##read it out to make sure it works ##Otherwise slows down routine. #row,col,data=np.loadtxt("noisyimage.txt",unpack=True) #rsize = int(max(row)) #csize = int(max(col)) #data=np.array(data).reshape(rsize,csize) # plt.imshow(data, interpolation='None',cmap=plt.cm.Greys_r)
def contour_edges(a): import matplotlib.pyplot as plt a = checkma(a) #Contour nodata value levels = [a.fill_value] #kw = {'antialiased':True, 'colors':'r', 'linestyles':'-'} kw = {'antialiased':True} #Generate contours around nodata cs = plt.contour(a.filled(), levels, **kw) #This returns a list of numpy arrays #allpts = np.vstack(cs.allsegs[0]) #Extract paths p = cs.collections[0].get_paths() #Sort by number of vertices in each path p_len = [i.vertices.shape[0] for i in p] p_sort = [x for (y,x) in sorted(zip(p_len,p), reverse=True)] #cp = p[0].make_compound_path(*p) return p_sort #Brute force search for edges of valid data
def save_goal_image(self, traj): rounded = np.around(traj.score, decimals=2) best_score = np.min(rounded) for i in range(traj.score.shape[0]): if rounded[i] == best_score: first_best_index = i break print 'best_score', best_score print 'allscores', traj.score print 'goal index: ', first_best_index goalimage = traj._sample_images[first_best_index] # goalstate = traj.X_Xdot_full[i] img = Image.fromarray(goalimage) cPickle.dump([], open(self._hyperparams['save_goal_image'] + '.pkl', 'wb')) img.save(self._hyperparams['save_goal_image'] + '.png',)
def evaluate_checkpoints(self, filename): # Load image with open(UPLOAD_FOLDER + '/' + filename, 'rb') as f: image_string = f.read() # Session with tf.Session() as sess: # Restore variables values self.saver.restore(sess, './models/'+self.loaded_model_name+'/model.ckpt') prob_values = sess.run(self.probs, feed_dict={ self.input_image_string: image_string }) pred_idx = prob_values[0].argsort()[-5:][::-1] pred_class = self.classes[pred_idx - 1] # from 1001 to 1000 classes pred_score = np.around(100*prob_values[0][pred_idx], decimals=2) # two decimals return list(pred_class), list(pred_score)
def evaluate_frozen(self, filename): # Load image with open(UPLOAD_FOLDER + '/' + filename, 'rb') as f: image_string = f.read() # Session with tf.Session() as sess: prob_values = sess.run(self.probs, feed_dict={ self.input_image_string: image_string }) pred_idx = prob_values[0].argsort()[-5:][::-1] pred_class = self.classes[pred_idx - 1] # from 1001 to 1000 classes pred_score = np.around(100*prob_values[0][pred_idx], decimals=2) # two decimals return list(pred_class), list(pred_score)
def histo_plot(figure,X,color,xlabel="",ylabel="",cumul=False,bar=True,n_points=400,smooth_factor=0.1,spline_order=3,linewidth=3,alpha=1.0,label=""): if '%' in xlabel: magnitude = 100 X_values = np.array(np.minimum(np.around(X),n_points+1),int) else: # magnitude = np.power(10,np.around(4*np.log10(X.mean()))/4+0.5) magnitude = np.power(10,np.around(4*np.log10(np.nanmean(X)+np.nanstd(X)+1e-7))/4+1) magnitude = np.around(magnitude,int(-np.log10(magnitude))+1) # print magnitude #magnitude = X.mean()+5.0*X.std() X_values = np.array(np.minimum(np.around(n_points*X[True-np.isnan(X)]/magnitude),n_points+1),int) X_histo = np.zeros(n_points+1,float) for x in np.linspace(0,n_points,n_points+1): X_histo[x] = nd.sum(np.ones_like(X_values,float),X_values,index=x) if '%' in ylabel: X_histo[x] /= X_values.size/100.0 if cumul: X_histo[x] += X_histo[x-1] if bar: bar_plot(figure,np.linspace(0,magnitude,n_points+1),X_histo,np.array([1,1,1]),color,xlabel,ylabel,label=label) else: smooth_plot(figure,np.linspace(0,magnitude,n_points+1),X_histo,color,color,xlabel,ylabel,n_points=n_points,smooth_factor=smooth_factor,spline_order=spline_order,linewidth=linewidth,alpha=alpha,label=label)
def make_pivots(start, stop, pivots_per_year=12, precision=2): """Makes an array of pivots (i.e., timepoints) between the given start and stop by the given pivots per year. The generated pivots are floating point values that are then rounded to the requested decimal precision. >>> list(make_pivots(2000.0, 2001.0, 5)) [2000.0, 2000.25, 2000.5, 2000.75, 2001.0] """ # Calculate number of pivots (i.e., months) in the requested interval. number_of_pivots = np.ceil((stop - start) * pivots_per_year) # Build an evenly-spaced closed interval (including the start and stop # points) based on the calculated number of pivots. return np.around( np.linspace(start, stop, number_of_pivots), precision )
def test_dayofwk(self): "Test computation of dayofwk in the 365_day calendar." converter = self.converters["noleap"] # Pick the date corresponding to the Julian day of 1.0 to test # the transision from positive to negative Julian days. julian_day = converter.date2num(datetimex(-4712, 1, 2, 12)) old_date = converter.num2date(julian_day) for delta_year in range(1, 101): # 100 years cover several 7-year cycles date = converter.num2date(julian_day - delta_year * 365) # test that the day of the week changes by one every year (except # for wrapping around every 7 years, of course) if date.dayofwk == 6: self.assertEqual(old_date.dayofwk, 0) else: self.assertEqual(old_date.dayofwk - date.dayofwk, 1) old_date = date
def test_reindex_nearest(self): s = Series(np.arange(10, dtype='int64')) target = [0.1, 0.9, 1.5, 2.0] actual = s.reindex(target, method='nearest') expected = Series(np.around(target).astype('int64'), target) assert_series_equal(expected, actual) actual = s.reindex_like(actual, method='nearest') assert_series_equal(expected, actual) actual = s.reindex_like(actual, method='nearest', tolerance=1) assert_series_equal(expected, actual) actual = s.reindex(target, method='nearest', tolerance=0.2) expected = Series([0, 1, np.nan, 2], target) assert_series_equal(expected, actual)
def almost(a, b, decimal=6, fill_value=True): """ Returns True if a and b are equal up to decimal places. If fill_value is True, masked values considered equal. Otherwise, masked values are considered unequal. """ m = mask_or(getmask(a), getmask(b)) d1 = filled(a) d2 = filled(b) if d1.dtype.char == "O" or d2.dtype.char == "O": return np.equal(d1, d2).ravel() x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_) y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_) d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal) return d.ravel()
def ylimit(self, value): """Upper and lower limit of current plot. Permanent limits are stored in yScaleSteps, which can be changed by user using range box. This is the only function through which plots can be updated. """ if value[0] == value[1]: return if not self.hiCmapAxis.doNotPlot: self._ylimit = [ value[0], value[1] ] # Setting yticks based on ylimits, only change yticks when y-limits are changed ydiff = value[0] - value[1] yticks = np.linspace(self.ylimit[0], self.ylimit[1], 100) # Set tick label according to precision self.yticksDecimals = gmlib.util.locate_significant_digit_after_decimal(ydiff) if self.yticksDecimals > 3: self.yticksFormatStyle = 'sci' self.yticks = np.around(yticks, decimals=self.yticksDecimals+1) else: self.yticks = np.around(yticks, decimals=self.yticksDecimals+1) self.updatePlot()
def project_radii(radii, spacing, r_min, r_max): """ Projects given radii to values between r_min and r_max; good spacing ~ 1000 """ radii_norm = radii / np.max(radii) # Normalize radii # Determine min and max of array and generate spacing radii_to_proj = np.around(np.linspace(np.min(radii_norm), np.max(radii_norm), spacing), 3) values_to_proj = np.around(np.linspace(r_min, r_max, spacing), 3) # Determine respective array positions pos = np.array([np.argmin(np.abs(radii_to_proj - radii_norm[entry])) for entry in range(len(radii_norm))], dtype=np.int) # Determine new radii return np.take(values_to_proj, pos) ############################################################################### # HUNGARIAN (MUNKRES) ALGORITHM - TAKEN FROM SCIPY ###############################################################################
def qreport(self, header="State", state=None, visualize=False): # This is only a simulator function for debugging. it CANNOT be done on a real Quantum Computer. if state == None: state = self.sys_state print print header for i in range(len(state)): if self.disp_zeros or np.absolute(state[i]) > self.maxerr: barlen = 20 barstr = "" if self.visualize or visualize: barstr = " x" amp = np.absolute(state[i].item(0))*barlen intamp = int(amp) if amp > self.maxerr: barstr = " |" for b in range(barlen): if b <= intamp: barstr = barstr+"*" else: barstr = barstr + "." ststr = ("{:0"+str(self.nqbits)+"b} ").format(i) ampstr = "{:.8f}".format(np.around(state[i].item(0),8)) print ststr + ampstr + barstr
def genLandmarkMap(landmarks, shape=[39, 39]): '''Generate landmark map according to landmarks. Input params: landmarks: K x 2 float shape: H x W Output: landmarkMap: H x W x K binary map. For each H x W map, there's only one location nearest to the landmark location filled with 1, else 0.''' landmarks = landmarks.reshape((-1, 2)) landmarkMap = np.zeros(shape + [len(landmarks)]) for (i, landmark) in enumerate(landmarks): x = int(np.around(landmark[0] * shape[1])) y = int(np.around(landmark[1] * shape[0])) landmarkMap[y, x, i] = 1 return landmarkMap
def show(img, name = "output.png"): """ Show MNSIT digits in the console. """ np.save(name, img) fig = np.around((img + 0.5)*255) fig = fig.astype(np.uint8).squeeze() pic = Image.fromarray(fig) # pic.resize((512,512), resample=PIL.Image.BICUBIC) pic.save(name) remap = " .*#"+"#"*100 img = (img.flatten()+.5)*3 if len(img) != 784: return print("START") for i in range(28): print("".join([remap[int(round(x))] for x in img[i*28:i*28+28]]))
