Curso Topograph 98 Se

[Chloe Sarnoff]

CURSODE TOPOGRAPH 98SE EM VDEO-AULASLevantamento Planialtimtrico em Geral* Como editar cadernetas de campo* Como definir a seqncia das poligonais* Como calcular poligonais (so mostrado dois mtodos diferentes no curso)* Como editar um desenho* Como criar a triangulao* Interpolando curvas* Como fazer a orientao geogrfica do seu desenho (levar o desenho dascoordenadas arbitrrias para as coordenadas geogrficas)* Como dividir uma propriedade em partes iguais (Ex. dividir uma fazendapara 4 herdeiros em partes iguais)* Como gerar plantas dos lotes (das partes divididas)* Como gerar Memoriais Descritivos dos lotesGeorreferenciamento de Imveis Rurais* Inserir novo DATUM ( mostrado como inserir o DATUM SIRGAS 2000, atual datum planimtrico brasileiro e usado pelo INCRA para georreferenciamento, no topoGRAPH, pois em algumas verses este DATUM no est incluso e voc aprender a inclui-lo caso na sua verso do topoGRAPH ele no exista.* Atualizar o arquivo do Modelo Magntico Terrestre (WMM.Cof) no topoGRAPH. (Cada modelo abrange um perodo de cinco anos, mostrado nesse curso como fazer a atualizao mais recente que vai de 2015 a 2020)* Definir as linhas de confrontantes* Renumerar os Vrtices altomaticamente j com o cdigo do credenciado* Configurar a Folha do Padro para os Parmetros da ABNT* Criar o Imvel* Inserir quadro Azimute/Distancia/Coordenadas* Inserir Orientao* Inserir Escala Grfica* Gerar a Planta do Imovel Georreferenciado* Gerar Memorial* Gerar Planilha* Gerar Monografia* Alterar a escala da Planta* Exportar Para o Autocad DXF/DWGComo iniciar um Novo Projeto de Estradas no TopoGRAPH a partir do Zero* Gerando Alinhamento para Estrada.( mostrado duas maneiras de se gerar o traado horizontal, sendo que uma das maneiras um macete pelo qual voc tem o domnio total e preciso de onde passar o seu traado).* Configurando o traado Horizontal.* Como Geral Perfil Longitudinal.* Como inserir e calcular o Traado Vertical* Calculo das Sees Transversais.* Definindo passo a passo e configurando Seo Tipo.( mostrado tambm como configurar outros elementos na seo tipo como por exemplo configurar uma sargeta ou valeta)* Como Calcular Volumes do Projeto, das Sees Corte e Aterro.* Carregando Traado Horizontal, e Visualizando o Projeto.* Editando o projeto, criando e processando linhas de quebra, inserindo as convenoes de talude em corte e em aterro. ( mostrado nesse processo como como quebrar as curvas de nvel dentro da plataforma e inserir as convenes de taludes de forma correta observando em cada seo onde exatamente so corte e aterro, deixando o projeto no padro pra ser entregue).* Criando a nota de servio de terraplenagem.( mostrado o passo a passo de como criar uma nova nota de servio, pois elas no vem prontas no topoGRAPH, e em seguida mostrado como gerar a nota de servio de terraplenagem do projeto calculado)* Calculando o volume entre dois terrenos pelo MDTO CURSO APRESENTADO NA SEGUNDA EDIO DO PROGRAMA topoGRAPH 98 SE VERSO 3.05

Destinado a profissionais que j tenham conhecimento bsico na rea de topografia, o curso visa ensinar o manuseio das ferramentas que compem o Topograph. Esse sistema , hoje, um dos mais utilizados no Brasil para processamento de dados topogrficos, clculos de volumes, projetos virios e elaborao de notas de servio.

Na mesma semana, Belo Horizonte (MG) recebe o curso de Topografia Aplicada, nos dias 10 e 11 de fevereiro. Destinado a profissionais das reas de engenharia, topografia, minerao, construo pesada e energia, este curso uma oportunidade para quem j trabalha na rea e deseja aprimorar seu currculo.

O curso, que dividido em duas etapas, visa apresentar os conceitos de topografia, diviso cartogrfica, grandezas angulares e lineares e planilha de coordenadas de levantamento topogrfico. Este contedo inicial alinhado prtica, realizada no Estao Total, em que os alunos podem aprender sobre metodologia aplicada, informaes tcnicas e funes do equipamento, levantamento de campo por coordenadas, locao de pontos, medio de pontos inacessveis, clculo de fechamento de poligonal, entre outros.

This tutorial shows how to perform standard ERP analyses in MNE-Python. Most ofthe material here is covered in other tutorials too, but for convenience thefunctions and methods most useful for ERP analyses are collected here, withlinks to other tutorials where more detailed information is given.

In practice it is quite common to have some channels labeled as EEG that areactually EOG channels. Raw objects have aset_channel_types() method that can be used to change achannel that is mislabeled as eeg to eog.

If you have digitized realistic sensor locations, there are dedicatedfunctions for loading those digitization files into MNE-Python (seeReading sensor digitization files for discussion and Supported formats for digitized 3D locations for a listof supported formats). Once loaded, the digitized sensor locations can beadded to the data by passing the loaded montage object toset_montage().

The referencing scheme can be changed with the functionmne.set_eeg_reference() (which by default operates on a copy of thedata) or the raw.set_eeg_reference()method (which always modifies the data in-place). The tutorialSetting the EEG reference shows several examples.

The general process for extracting evoked responses from continuous data isto use the Epochs constructor, and then average the resultingepochs to create an Evoked object. In MNE-Python, events arerepresented as a NumPy array containing eventlatencies (in samples) and integer event codes. The event codes are stored inthe last column of the events array:

The Working with events tutorial discusses event arrays in more detail.Integer event codes are mapped to more descriptive text using a Pythondictionary usually called event_id. This mapping isdetermined by your experiment (i.e., it reflects which event codes you choseto represent different experimental events or conditions). TheSample data uses the following mapping:

It is also possible to automatically drop epochs (either when first creatingthem or later on) by providing maximum peak-to-peak signal value thresholds(passed to Epochs as the reject parameter; seeRejecting Epochs based on peak-to-peak channel amplitude for details). You can also do this afterthe epochs are already created using drop_bad():

Scalp topographies can also be obtained non-interactively with theplot_topomap() method. Here, we display topomaps of theaverage evoked potential in 50 ms time windows centered at -200 ms, 100 ms,and 400 ms.

There is also a built-in method for combining butterfly plots of the signalswith scalp topographies called plot_joint(). Like inplot_topomap(), you can specify times for the scalptopographies or you can let the method choose times automatically as shownhere:

The GFP is the population standard deviation of the signalacross channels. To compute it manually, we can leverage the fact thatevoked.data is a NumPy array,and verify by plotting it using plain Matplotlib commands:

Since our sample data contains responses to left and right auditory andvisual stimuli, we may want to compare left versus right regions of interest(ROIs). To average across channels in a given ROI, we first find the relevantchannel indices. Revisiting the 2D sensor plot above, we might choose thefollowing channels for left and right ROIs, respectively:

If we wanted to contrast auditory to visual stimuli, a useful function ismne.viz.plot_compare_evokeds(). By default, this function will combineall channels in each evoked object using GFP (or RMS for MEG channels); hereinstead we specify to combine by averaging, and restrict it to a subset ofchannels by passing picks:

We can also generate confidence intervals by treating each epoch as aseparate observation using iter_evoked(). A confidenceinterval across subjects could also be obtained by passing a list ofEvoked objects (one per subject) to theplot_compare_evokeds() function.

The code above yields an equal-weighted difference. If you havedifferent numbers of epochs per condition, you might want to equalize thenumber of events per condition first by usingepochs.equalize_event_counts()before averaging.

It is common in ERP research to extract measures of amplitude or latency tocompare across different conditions. There are many measures that can beextracted from ERPs, and many of these are detailed (including the respectivestrengths and weaknesses) in chapter 9 of Luck [4] (also seethe Measurement Tool in the ERPLAB Toolbox[5]).

The most common measures of amplitude and latency are peak measures.Peak measures are basically the maximum amplitude of the signal in aspecified time window and the time point (or latency) at which the peakamplitude occurred.

Peak measures can be obtained using the get_peak() method.There are two important things to point out aboutget_peak(). First, it finds the strongest peaklooking across all channels of the selected type that are available inthe Evoked object. As a consequence, if you want to restrictthe search to a group of channels or a single channel, youshould first use the pick() orpick_channels() methods. Second, theget_peak() method can find different types of peaks usingthe mode argument. There are three options:

The following example demonstrates how to find the first positive peak in theERP (i.e., the P100) for the left visual condition (i.e., thel_vis Evoked object). The time window used to search forthe peak ranges from 0.08 to 0.12 s. This time window was selected because itis when P100 typically occurs. Note that all 'eeg' channels are submittedto the get_peak() method.

The output shows that channel eeg55 had the maximum positive peak inthe chosen time window from all of the 'eeg' channels searched.In practice, one might want to pull out the peak foran a priori region of interest or a single channel depending on the study.This can be done by combining the pick()or pick_channels() methods with theget_peak() method.

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